@mastra/server - npm Package Compare versions

Comparing version 1.55.0 to 1.56.0-alpha.0

dist/datasets-BGbh4uCo.cjs

+const require_common = require("./common-spbwhiWS.cjs");
+let zod_v4 = require("zod/v4");
+//#region src/server/schemas/datasets.ts
+const jsonSchemaObject = zod_v4.z.lazy(() => zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()));
+const jsonSchemaField = zod_v4.z.union([jsonSchemaObject, zod_v4.z.null()]).optional();
+const expectedStepBase = {
+	name: zod_v4.z.string().describe("Step name to match"),
+	durationMs: zod_v4.z.number().optional(),
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	children: zod_v4.z.unknown().optional().describe("Nested trajectory expectation (untyped at this depth)")
+};
+const expectedToolCallStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("tool_call"),
+	toolArgs: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	toolResult: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	success: zod_v4.z.boolean().optional()
+});
+const expectedMcpToolCallStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("mcp_tool_call"),
+	toolArgs: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	toolResult: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	mcpServer: zod_v4.z.string().optional(),
+	success: zod_v4.z.boolean().optional()
+});
+const expectedModelGenerationStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("model_generation"),
+	modelId: zod_v4.z.string().optional(),
+	promptTokens: zod_v4.z.number().optional(),
+	completionTokens: zod_v4.z.number().optional(),
+	finishReason: zod_v4.z.string().optional()
+});
+const expectedAgentRunStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("agent_run"),
+	agentId: zod_v4.z.string().optional()
+});
+const expectedWorkflowStepStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("workflow_step"),
+	stepId: zod_v4.z.string().optional(),
+	status: zod_v4.z.string().optional(),
+	output: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional()
+});
+const expectedWorkflowRunStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("workflow_run"),
+	workflowId: zod_v4.z.string().optional(),
+	status: zod_v4.z.string().optional()
+});
+const expectedWorkflowConditionalStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("workflow_conditional"),
+	conditionCount: zod_v4.z.number().optional(),
+	selectedSteps: zod_v4.z.array(zod_v4.z.string()).optional()
+});
+const expectedWorkflowParallelStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("workflow_parallel"),
+	branchCount: zod_v4.z.number().optional(),
+	parallelSteps: zod_v4.z.array(zod_v4.z.string()).optional()
+});
+const expectedWorkflowLoopStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("workflow_loop"),
+	loopType: zod_v4.z.string().optional(),
+	totalIterations: zod_v4.z.number().optional()
+});
+const expectedWorkflowSleepStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("workflow_sleep"),
+	sleepDurationMs: zod_v4.z.number().optional(),
+	sleepType: zod_v4.z.string().optional()
+});
+const expectedWorkflowWaitEventStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("workflow_wait_event"),
+	eventName: zod_v4.z.string().optional(),
+	eventReceived: zod_v4.z.boolean().optional()
+});
+const expectedProcessorRunStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.literal("processor_run"),
+	processorId: zod_v4.z.string().optional()
+});
+const expectedGenericStepSchema = zod_v4.z.object({
+	...expectedStepBase,
+	stepType: zod_v4.z.undefined().optional()
+});
+const expectedStepSchema = zod_v4.z.union([zod_v4.z.discriminatedUnion("stepType", [
+	expectedToolCallStepSchema,
+	expectedMcpToolCallStepSchema,
+	expectedModelGenerationStepSchema,
+	expectedAgentRunStepSchema,
+	expectedWorkflowStepStepSchema,
+	expectedWorkflowRunStepSchema,
+	expectedWorkflowConditionalStepSchema,
+	expectedWorkflowParallelStepSchema,
+	expectedWorkflowLoopStepSchema,
+	expectedWorkflowSleepStepSchema,
+	expectedWorkflowWaitEventStepSchema,
+	expectedProcessorRunStepSchema
+]), expectedGenericStepSchema]);
+const trajectoryExpectationSchema = zod_v4.z.object({
+	steps: zod_v4.z.array(expectedStepSchema).optional().describe("Expected steps for accuracy checking"),
+	ordering: zod_v4.z.enum([
+		"strict",
+		"relaxed",
+		"unordered"
+	]).optional().describe("How to compare step ordering (default: relaxed)"),
+	allowRepeatedSteps: zod_v4.z.boolean().optional().describe("Whether to allow repeated steps (default: true)"),
+	maxSteps: zod_v4.z.number().int().optional().describe("Maximum number of steps allowed"),
+	maxTotalTokens: zod_v4.z.number().int().optional().describe("Maximum total tokens across all model_generation steps"),
+	maxTotalDurationMs: zod_v4.z.number().optional().describe("Maximum total duration in milliseconds"),
+	noRedundantCalls: zod_v4.z.boolean().optional().describe("Whether to penalize redundant calls (same tool + same args consecutively, default: true)"),
+	blacklistedTools: zod_v4.z.array(zod_v4.z.string()).optional().describe("Tool names that should never appear"),
+	blacklistedSequences: zod_v4.z.array(zod_v4.z.array(zod_v4.z.string())).optional().describe("Tool name sequences that should never appear"),
+	maxRetriesPerTool: zod_v4.z.number().int().optional().describe("Maximum retries per tool before penalizing (default: 2)")
+}).optional().nullable().describe("Expected trajectory configuration for trajectory scoring");
+const datasetItemSourceSchema = zod_v4.z.object({
+	type: zod_v4.z.enum([
+		"csv",
+		"json",
+		"trace",
+		"llm",
+		"experiment-result",
+		"candidate-screener"
+	]).describe("How this item was created"),
+	referenceId: zod_v4.z.string().optional().describe("Reference identifier (e.g., trace id, csv filename)")
+}).optional().describe("Source/provenance of this dataset item");
+const itemToolMockSchema = zod_v4.z.object({
+	toolName: zod_v4.z.string().describe("Name of the tool this mock applies to"),
+	args: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).describe("Arguments to match against the tool call"),
+	output: zod_v4.z.unknown().describe("Output served to the agent when matched"),
+	matchArgs: zod_v4.z.enum(["strict", "ignore"]).optional().describe("Argument matching mode. 'strict' (default) deep-equals args; 'ignore' matches on toolName only")
+});
+const toolMocksSchema = zod_v4.z.array(itemToolMockSchema).optional().describe("Ordered item-level static tool mocks served in place of executing the real tool");
+const unmockedToolPolicySchema = zod_v4.z.enum(["allow", "deny"]).optional().describe("Policy for undeclared tool calls. 'allow' runs them live; 'deny' fails the experiment item");
+const toolMockReportSchema = zod_v4.z.object({
+	served: zod_v4.z.array(zod_v4.z.object({
+		mockIndex: zod_v4.z.number().int(),
+		toolName: zod_v4.z.string(),
+		args: zod_v4.z.unknown()
+	})),
+	unconsumed: zod_v4.z.array(zod_v4.z.object({
+		mockIndex: zod_v4.z.number().int(),
+		toolName: zod_v4.z.string(),
+		args: zod_v4.z.unknown()
+	})),
+	liveCalls: zod_v4.z.array(zod_v4.z.object({
+		toolName: zod_v4.z.string(),
+		args: zod_v4.z.unknown()
+	})),
+	failure: zod_v4.z.object({
+		code: zod_v4.z.enum([
+			"TOOL_MOCK_MISMATCH",
+			"TOOL_MOCK_EXHAUSTED",
+			"TOOL_MOCK_NOT_DECLARED"
+		]),
+		toolName: zod_v4.z.string(),
+		args: zod_v4.z.unknown()
+	}).optional()
+}).optional().describe("Diagnostic receipt for item-level tool mocks");
+const datasetIdPathParams = zod_v4.z.object({ datasetId: zod_v4.z.string().describe("Unique identifier for the dataset") });
+const experimentIdPathParams = zod_v4.z.object({ experimentId: zod_v4.z.string().describe("Unique identifier for the experiment") });
+const itemIdPathParams = zod_v4.z.object({ itemId: zod_v4.z.string().describe("Unique identifier for the dataset item") });
+const datasetAndExperimentIdPathParams = zod_v4.z.object({
+	datasetId: zod_v4.z.string().describe("Unique identifier for the dataset"),
+	experimentId: zod_v4.z.string().describe("Unique identifier for the experiment")
+});
+const experimentResultIdPathParams = zod_v4.z.object({
+	datasetId: zod_v4.z.string().describe("Unique identifier for the dataset"),
+	experimentId: zod_v4.z.string().describe("Unique identifier for the experiment"),
+	resultId: zod_v4.z.string().describe("Unique identifier for the experiment result")
+});
+const datasetAndItemIdPathParams = zod_v4.z.object({
+	datasetId: zod_v4.z.string().describe("Unique identifier for the dataset"),
+	itemId: zod_v4.z.string().describe("Unique identifier for the dataset item")
+});
+const paginationQuerySchema = zod_v4.z.object({
+	page: zod_v4.z.coerce.number().optional().default(0),
+	perPage: zod_v4.z.coerce.number().optional().default(10)
+});
+const tenancyQuerySchema = zod_v4.z.object({
+	organizationId: zod_v4.z.string().optional().describe("Restrict lookup to the given organization"),
+	projectId: zod_v4.z.string().optional().describe("Restrict lookup to the given project")
+});
+const listItemsQuerySchema = zod_v4.z.object({
+	page: zod_v4.z.coerce.number().optional().default(0),
+	perPage: zod_v4.z.coerce.number().optional().default(10),
+	version: zod_v4.z.coerce.number().int().optional(),
+	search: zod_v4.z.string().optional()
+});
+const createDatasetBodySchema = zod_v4.z.object({
+	name: zod_v4.z.string().describe("Name of the dataset"),
+	description: zod_v4.z.string().optional().describe("Description of the dataset"),
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Additional metadata"),
+	inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
+	groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
+	requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
+	targetType: zod_v4.z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
+	targetIds: zod_v4.z.array(zod_v4.z.string()).optional().describe("IDs of target entities this dataset is attached to"),
+	scorerIds: zod_v4.z.array(zod_v4.z.string()).optional().describe("IDs of scorers attached to this dataset")
+});
+const updateDatasetBodySchema = zod_v4.z.object({
+	name: zod_v4.z.string().optional().describe("Name of the dataset"),
+	description: zod_v4.z.string().optional().describe("Description of the dataset"),
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Additional metadata"),
+	inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
+	groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
+	requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
+	tags: zod_v4.z.array(zod_v4.z.string()).optional().describe("Tag definitions for categorizing experiment results"),
+	targetType: zod_v4.z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
+	targetIds: zod_v4.z.array(zod_v4.z.string()).optional().describe("IDs of target entities this dataset is attached to"),
+	scorerIds: zod_v4.z.array(zod_v4.z.string()).optional().nullable().describe("IDs of scorers attached to this dataset")
+});
+const addItemBodySchema = zod_v4.z.object({
+	externalId: zod_v4.z.string().optional().nullable().describe("Caller-defined, dataset-local item identity"),
+	input: zod_v4.z.unknown().describe("Input data for the dataset item"),
+	groundTruth: zod_v4.z.unknown().optional().describe("Expected output for comparison"),
+	expectedTrajectory: trajectoryExpectationSchema,
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Request context preset for this item"),
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Additional metadata"),
+	source: datasetItemSourceSchema
+});
+const updateItemBodySchema = zod_v4.z.object({
+	input: zod_v4.z.unknown().optional().describe("Input data for the dataset item"),
+	groundTruth: zod_v4.z.unknown().optional().describe("Expected output for comparison"),
+	expectedTrajectory: trajectoryExpectationSchema,
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Request context preset for this item"),
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Additional metadata"),
+	source: datasetItemSourceSchema
+});
+const triggerExperimentBodySchema = zod_v4.z.object({
+	targetType: zod_v4.z.enum([
+		"agent",
+		"workflow",
+		"scorer"
+	]).describe("Type of target to run against"),
+	targetId: zod_v4.z.string().describe("ID of the target"),
+	scorerIds: zod_v4.z.array(zod_v4.z.string()).optional().describe("IDs of scorers to apply"),
+	version: zod_v4.z.coerce.number().int().optional().describe("Pin to specific dataset version"),
+	agentVersion: zod_v4.z.string().optional().describe("Agent version ID to use for experiment"),
+	maxConcurrency: zod_v4.z.number().optional().describe("Maximum concurrent executions"),
+	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Global request context passed to the target"),
+	versions: zod_v4.z.object({
+		agents: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.union([zod_v4.z.object({ versionId: zod_v4.z.string() }), zod_v4.z.object({ status: zod_v4.z.enum(["draft", "published"]) })])).optional(),
+		defaultStatus: zod_v4.z.enum(["draft", "published"]).optional()
+	}).optional().describe("Version overrides for sub-agent delegation during experiment execution")
+});
+const compareExperimentsBodySchema = zod_v4.z.object({
+	experimentIdA: zod_v4.z.string().describe("ID of baseline experiment"),
+	experimentIdB: zod_v4.z.string().describe("ID of candidate experiment")
+});
+const datasetResponseSchema = zod_v4.z.object({
+	id: zod_v4.z.string(),
+	name: zod_v4.z.string(),
+	description: zod_v4.z.string().optional().nullable(),
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().nullable(),
+	inputSchema: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	groundTruthSchema: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	requestContextSchema: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	tags: zod_v4.z.array(zod_v4.z.string()).optional().nullable(),
+	targetType: zod_v4.z.string().optional().nullable(),
+	targetIds: zod_v4.z.array(zod_v4.z.string()).optional().nullable(),
+	scorerIds: zod_v4.z.array(zod_v4.z.string()).optional().nullable(),
+	version: zod_v4.z.number().int(),
+	createdAt: zod_v4.z.coerce.date(),
+	updatedAt: zod_v4.z.coerce.date()
+});
+const datasetItemResponseSchema = zod_v4.z.object({
+	id: zod_v4.z.string(),
+	datasetId: zod_v4.z.string(),
+	datasetVersion: zod_v4.z.number().int(),
+	externalId: zod_v4.z.string().optional().nullable(),
+	input: zod_v4.z.unknown(),
+	groundTruth: zod_v4.z.unknown().optional(),
+	expectedTrajectory: zod_v4.z.unknown().optional(),
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	source: datasetItemSourceSchema,
+	createdAt: zod_v4.z.coerce.date(),
+	updatedAt: zod_v4.z.coerce.date()
+});
+const experimentResponseSchema = zod_v4.z.object({
+	id: zod_v4.z.string(),
+	datasetId: zod_v4.z.string().nullable(),
+	datasetVersion: zod_v4.z.number().int().nullable(),
+	agentVersion: zod_v4.z.string().nullable().optional(),
+	targetType: zod_v4.z.enum([
+		"agent",
+		"workflow",
+		"scorer",
+		"processor"
+	]),
+	targetId: zod_v4.z.string(),
+	name: zod_v4.z.string().optional(),
+	description: zod_v4.z.string().optional(),
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	status: zod_v4.z.enum([
+		"pending",
+		"running",
+		"completed",
+		"failed"
+	]),
+	totalItems: zod_v4.z.number(),
+	succeededCount: zod_v4.z.number(),
+	failedCount: zod_v4.z.number(),
+	skippedCount: zod_v4.z.number(),
+	startedAt: zod_v4.z.coerce.date().nullable(),
+	completedAt: zod_v4.z.coerce.date().nullable(),
+	createdAt: zod_v4.z.coerce.date(),
+	updatedAt: zod_v4.z.coerce.date()
+});
+const scorerResultSchema = zod_v4.z.object({
+	scorerId: zod_v4.z.string(),
+	scorerName: zod_v4.z.string(),
+	score: zod_v4.z.number().nullable(),
+	reason: zod_v4.z.string().nullable(),
+	error: zod_v4.z.string().nullable()
+});
+const experimentResultResponseSchema = zod_v4.z.object({
+	id: zod_v4.z.string(),
+	experimentId: zod_v4.z.string(),
+	itemId: zod_v4.z.string(),
+	itemDatasetVersion: zod_v4.z.number().int().nullable(),
+	input: zod_v4.z.unknown(),
+	output: zod_v4.z.unknown().nullable(),
+	groundTruth: zod_v4.z.unknown().nullable(),
+	expectedTrajectory: zod_v4.z.unknown().optional(),
+	error: zod_v4.z.object({
+		message: zod_v4.z.string(),
+		stack: zod_v4.z.string().optional(),
+		code: zod_v4.z.string().optional()
+	}).nullable(),
+	startedAt: zod_v4.z.coerce.date(),
+	completedAt: zod_v4.z.coerce.date(),
+	retryCount: zod_v4.z.number(),
+	traceId: zod_v4.z.string().nullable(),
+	status: zod_v4.z.enum([
+		"needs-review",
+		"reviewed",
+		"complete"
+	]).nullable().optional(),
+	tags: zod_v4.z.array(zod_v4.z.string()).nullable().optional(),
+	toolMockReport: toolMockReportSchema.nullable(),
+	createdAt: zod_v4.z.coerce.date()
+});
+const updateExperimentResultBodySchema = zod_v4.z.object({
+	status: zod_v4.z.enum([
+		"needs-review",
+		"reviewed",
+		"complete"
+	]).nullable().optional(),
+	tags: zod_v4.z.array(zod_v4.z.string()).optional()
+});
+const comparisonItemSchema = zod_v4.z.object({
+	itemId: zod_v4.z.string(),
+	input: zod_v4.z.unknown().nullable(),
+	groundTruth: zod_v4.z.unknown().nullable(),
+	results: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.object({
+		output: zod_v4.z.unknown().nullable(),
+		scores: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.number().nullable())
+	}).nullable())
+});
+const comparisonResponseSchema = zod_v4.z.object({
+	baselineId: zod_v4.z.string(),
+	items: zod_v4.z.array(comparisonItemSchema)
+});
+const experimentSummaryResponseSchema = zod_v4.z.object({
+	experimentId: zod_v4.z.string(),
+	status: zod_v4.z.enum([
+		"pending",
+		"running",
+		"completed",
+		"failed"
+	]),
+	totalItems: zod_v4.z.number(),
+	succeededCount: zod_v4.z.number(),
+	failedCount: zod_v4.z.number(),
+	startedAt: zod_v4.z.coerce.date(),
+	completedAt: zod_v4.z.coerce.date().nullable(),
+	results: zod_v4.z.array(zod_v4.z.object({
+		itemId: zod_v4.z.string(),
+		itemDatasetVersion: zod_v4.z.number().int().nullable(),
+		input: zod_v4.z.unknown(),
+		output: zod_v4.z.unknown().nullable(),
+		groundTruth: zod_v4.z.unknown().nullable(),
+		error: zod_v4.z.string().nullable(),
+		startedAt: zod_v4.z.coerce.date(),
+		completedAt: zod_v4.z.coerce.date(),
+		retryCount: zod_v4.z.number(),
+		toolMockReport: toolMockReportSchema.nullable(),
+		scores: zod_v4.z.array(zod_v4.z.object({
+			scorerId: zod_v4.z.string(),
+			scorerName: zod_v4.z.string(),
+			score: zod_v4.z.number().nullable(),
+			reason: zod_v4.z.string().nullable(),
+			error: zod_v4.z.string().nullable()
+		}))
+	}))
+});
+const listDatasetsResponseSchema = zod_v4.z.object({
+	datasets: zod_v4.z.array(datasetResponseSchema),
+	pagination: require_common.paginationInfoSchema
+});
+const listItemsResponseSchema = zod_v4.z.object({
+	items: zod_v4.z.array(datasetItemResponseSchema),
+	pagination: require_common.paginationInfoSchema
+});
+const listExperimentsResponseSchema = zod_v4.z.object({
+	experiments: zod_v4.z.array(experimentResponseSchema),
+	pagination: require_common.paginationInfoSchema
+});
+const listExperimentResultsResponseSchema = zod_v4.z.object({
+	results: zod_v4.z.array(experimentResultResponseSchema),
+	pagination: require_common.paginationInfoSchema
+});
+const experimentReviewCountsSchema = zod_v4.z.object({
+	experimentId: zod_v4.z.string(),
+	total: zod_v4.z.number().int(),
+	needsReview: zod_v4.z.number().int(),
+	reviewed: zod_v4.z.number().int(),
+	complete: zod_v4.z.number().int()
+});
+const reviewSummaryResponseSchema = zod_v4.z.object({ counts: zod_v4.z.array(experimentReviewCountsSchema) });
+const datasetItemVersionPathParams = zod_v4.z.object({
+	datasetId: zod_v4.z.string().describe("Unique identifier for the dataset"),
+	itemId: zod_v4.z.string().describe("Unique identifier for the dataset item"),
+	datasetVersion: zod_v4.z.coerce.number().int().describe("Dataset version number")
+});
+const itemVersionResponseSchema = zod_v4.z.object({
+	id: zod_v4.z.string(),
+	datasetId: zod_v4.z.string(),
+	datasetVersion: zod_v4.z.number().int(),
+	input: zod_v4.z.unknown(),
+	groundTruth: zod_v4.z.unknown().optional(),
+	expectedTrajectory: zod_v4.z.unknown().optional(),
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	validTo: zod_v4.z.number().int().nullable(),
+	isDeleted: zod_v4.z.boolean(),
+	createdAt: zod_v4.z.coerce.date(),
+	updatedAt: zod_v4.z.coerce.date()
+});
+const listItemVersionsResponseSchema = zod_v4.z.object({ history: zod_v4.z.array(itemVersionResponseSchema) });
+const datasetVersionResponseSchema = zod_v4.z.object({
+	id: zod_v4.z.string(),
+	datasetId: zod_v4.z.string(),
+	version: zod_v4.z.number().int(),
+	createdAt: zod_v4.z.coerce.date()
+});
+const listDatasetVersionsResponseSchema = zod_v4.z.object({
+	versions: zod_v4.z.array(datasetVersionResponseSchema),
+	pagination: require_common.paginationInfoSchema
+});
+const batchInsertItemsBodySchema = zod_v4.z.object({ items: zod_v4.z.array(zod_v4.z.object({
+	externalId: zod_v4.z.string().optional().nullable(),
+	input: zod_v4.z.unknown(),
+	groundTruth: zod_v4.z.unknown().optional(),
+	expectedTrajectory: trajectoryExpectationSchema,
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
+	source: datasetItemSourceSchema
+})) });
+const batchInsertItemsResponseSchema = zod_v4.z.object({
+	items: zod_v4.z.array(datasetItemResponseSchema),
+	count: zod_v4.z.number()
+});
+const batchDeleteItemsBodySchema = zod_v4.z.object({ itemIds: zod_v4.z.array(zod_v4.z.string()) });
+const batchDeleteItemsResponseSchema = zod_v4.z.object({
+	success: zod_v4.z.boolean(),
+	deletedCount: zod_v4.z.number()
+});
+const generateItemsBodySchema = zod_v4.z.object({
+	modelId: zod_v4.z.string().describe("Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")"),
+	prompt: zod_v4.z.string().describe("Description of the kind of test data to generate"),
+	count: zod_v4.z.number().int().min(1).max(50).default(5).describe("Number of items to generate"),
+	agentContext: zod_v4.z.object({
+		description: zod_v4.z.string().optional(),
+		instructions: zod_v4.z.string().optional(),
+		tools: zod_v4.z.array(zod_v4.z.string()).optional()
+	}).optional().describe("Context about the agent to generate relevant test data")
+});
+const generatedItemSchema = zod_v4.z.object({
+	input: zod_v4.z.unknown(),
+	groundTruth: zod_v4.z.unknown().optional()
+});
+const generateItemsResponseSchema = zod_v4.z.object({ items: zod_v4.z.array(generatedItemSchema) });
+const clusterFailuresBodySchema = zod_v4.z.object({
+	modelId: zod_v4.z.string().describe("Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")"),
+	items: zod_v4.z.array(zod_v4.z.object({
+		id: zod_v4.z.string(),
+		input: zod_v4.z.unknown(),
+		output: zod_v4.z.unknown().optional(),
+		error: zod_v4.z.string().optional(),
+		scores: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.number()).optional(),
+		existingTags: zod_v4.z.array(zod_v4.z.string()).optional().describe("Tags already applied to this item")
+	})).min(1).max(200).describe("Failure items to cluster"),
+	availableTags: zod_v4.z.array(zod_v4.z.string()).optional().describe("Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable."),
+	prompt: zod_v4.z.string().optional().describe("Optional user instructions to guide the analysis (e.g., \"focus on tool usage failures\")")
+});
+const failureClusterSchema = zod_v4.z.object({
+	id: zod_v4.z.string().describe("A unique cluster identifier"),
+	label: zod_v4.z.string().describe("Short label for this failure pattern"),
+	description: zod_v4.z.string().describe("Description of the common pattern"),
+	itemIds: zod_v4.z.array(zod_v4.z.string()).describe("IDs of items belonging to this cluster")
+});
+const clusterFailuresResponseSchema = zod_v4.z.object({
+	clusters: zod_v4.z.array(failureClusterSchema),
+	/** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */
+	proposedTags: zod_v4.z.array(zod_v4.z.object({
+		itemId: zod_v4.z.string(),
+		tags: zod_v4.z.array(zod_v4.z.string()),
+		reason: zod_v4.z.string().describe("Brief explanation of why these tags were assigned to this item")
+	})).optional()
+});
+//#endregion
+Object.defineProperty(exports, "addItemBodySchema", {
+	enumerable: true,
+	get: function() {
+		return addItemBodySchema;
+	}
+});
+Object.defineProperty(exports, "batchDeleteItemsBodySchema", {
+	enumerable: true,
+	get: function() {
+		return batchDeleteItemsBodySchema;
+	}
+});
+Object.defineProperty(exports, "batchDeleteItemsResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return batchDeleteItemsResponseSchema;
+	}
+});
+Object.defineProperty(exports, "batchInsertItemsBodySchema", {
+	enumerable: true,
+	get: function() {
+		return batchInsertItemsBodySchema;
+	}
+});
+Object.defineProperty(exports, "batchInsertItemsResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return batchInsertItemsResponseSchema;
+	}
+});
+Object.defineProperty(exports, "clusterFailuresBodySchema", {
+	enumerable: true,
+	get: function() {
+		return clusterFailuresBodySchema;
+	}
+});
+Object.defineProperty(exports, "clusterFailuresResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return clusterFailuresResponseSchema;
+	}
+});
+Object.defineProperty(exports, "compareExperimentsBodySchema", {
+	enumerable: true,
+	get: function() {
+		return compareExperimentsBodySchema;
+	}
+});
+Object.defineProperty(exports, "comparisonResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return comparisonResponseSchema;
+	}
+});
+Object.defineProperty(exports, "createDatasetBodySchema", {
+	enumerable: true,
+	get: function() {
+		return createDatasetBodySchema;
+	}
+});
+Object.defineProperty(exports, "datasetAndExperimentIdPathParams", {
+	enumerable: true,
+	get: function() {
+		return datasetAndExperimentIdPathParams;
+	}
+});
+Object.defineProperty(exports, "datasetAndItemIdPathParams", {
+	enumerable: true,
+	get: function() {
+		return datasetAndItemIdPathParams;
+	}
+});
+Object.defineProperty(exports, "datasetIdPathParams", {
+	enumerable: true,
+	get: function() {
+		return datasetIdPathParams;
+	}
+});
+Object.defineProperty(exports, "datasetItemResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return datasetItemResponseSchema;
+	}
+});
+Object.defineProperty(exports, "datasetItemVersionPathParams", {
+	enumerable: true,
+	get: function() {
+		return datasetItemVersionPathParams;
+	}
+});
+Object.defineProperty(exports, "datasetResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return datasetResponseSchema;
+	}
+});
+Object.defineProperty(exports, "datasetVersionResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return datasetVersionResponseSchema;
+	}
+});
+Object.defineProperty(exports, "experimentIdPathParams", {
+	enumerable: true,
+	get: function() {
+		return experimentIdPathParams;
+	}
+});
+Object.defineProperty(exports, "experimentResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return experimentResponseSchema;
+	}
+});
+Object.defineProperty(exports, "experimentResultIdPathParams", {
+	enumerable: true,
+	get: function() {
+		return experimentResultIdPathParams;
+	}
+});
+Object.defineProperty(exports, "experimentResultResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return experimentResultResponseSchema;
+	}
+});
+Object.defineProperty(exports, "experimentReviewCountsSchema", {
+	enumerable: true,
+	get: function() {
+		return experimentReviewCountsSchema;
+	}
+});
+Object.defineProperty(exports, "experimentSummaryResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return experimentSummaryResponseSchema;
+	}
+});
+Object.defineProperty(exports, "generateItemsBodySchema", {
+	enumerable: true,
+	get: function() {
+		return generateItemsBodySchema;
+	}
+});
+Object.defineProperty(exports, "generateItemsResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return generateItemsResponseSchema;
+	}
+});
+Object.defineProperty(exports, "itemIdPathParams", {
+	enumerable: true,
+	get: function() {
+		return itemIdPathParams;
+	}
+});
+Object.defineProperty(exports, "itemVersionResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return itemVersionResponseSchema;
+	}
+});
+Object.defineProperty(exports, "listDatasetVersionsResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return listDatasetVersionsResponseSchema;
+	}
+});
+Object.defineProperty(exports, "listDatasetsResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return listDatasetsResponseSchema;
+	}
+});
+Object.defineProperty(exports, "listExperimentResultsResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return listExperimentResultsResponseSchema;
+	}
+});
+Object.defineProperty(exports, "listExperimentsResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return listExperimentsResponseSchema;
+	}
+});
+Object.defineProperty(exports, "listItemVersionsResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return listItemVersionsResponseSchema;
+	}
+});
+Object.defineProperty(exports, "listItemsQuerySchema", {
+	enumerable: true,
+	get: function() {
+		return listItemsQuerySchema;
+	}
+});
+Object.defineProperty(exports, "listItemsResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return listItemsResponseSchema;
+	}
+});
+Object.defineProperty(exports, "paginationQuerySchema", {
+	enumerable: true,
+	get: function() {
+		return paginationQuerySchema;
+	}
+});
+Object.defineProperty(exports, "reviewSummaryResponseSchema", {
+	enumerable: true,
+	get: function() {
+		return reviewSummaryResponseSchema;
+	}
+});
+Object.defineProperty(exports, "scorerResultSchema", {
+	enumerable: true,
+	get: function() {
+		return scorerResultSchema;
+	}
+});
+Object.defineProperty(exports, "tenancyQuerySchema", {
+	enumerable: true,
+	get: function() {
+		return tenancyQuerySchema;
+	}
+});
+Object.defineProperty(exports, "triggerExperimentBodySchema", {
+	enumerable: true,
+	get: function() {
+		return triggerExperimentBodySchema;
+	}
+});
+Object.defineProperty(exports, "updateDatasetBodySchema", {
+	enumerable: true,
+	get: function() {
+		return updateDatasetBodySchema;
+	}
+});
+Object.defineProperty(exports, "updateExperimentResultBodySchema", {
+	enumerable: true,
+	get: function() {
+		return updateExperimentResultBodySchema;
+	}
+});
+Object.defineProperty(exports, "updateItemBodySchema", {
+	enumerable: true,
+	get: function() {
+		return updateItemBodySchema;
+	}
+});
+
+//# sourceMappingURL=datasets-BGbh4uCo.cjs.map

dist/datasets-BGbh4uCo.cjs.map

+{"version":3,"file":"datasets-BGbh4uCo.cjs","names":["z","paginationInfoSchema"],"sources":["../src/server/schemas/datasets.ts"],"sourcesContent":["import { z } from 'zod/v4';\nimport { paginationInfoSchema } from './common';\n\n// ============================================================================\n// JSON Schema Types (for inputSchema/groundTruthSchema fields)\n// ============================================================================\n\n// JSON Schema type (simplified for storage - full spec too complex)\nconst jsonSchemaObject: z.ZodType<Record<string, unknown>> = z.lazy(() => z.record(z.string(), z.unknown()));\n\n// JSON Schema field (object or null to disable)\nconst jsonSchemaField = z.union([jsonSchemaObject, z.null()]).optional();\n\n// ============================================================================\n// Trajectory Expectation Schema (2 levels deep, children at level 2 use z.unknown())\n// ============================================================================\n\n// Shared base fields for expected steps (level 2 — children typed as z.unknown())\nconst expectedStepBase = {\n  name: z.string().describe('Step name to match'),\n  durationMs: z.number().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  children: z.unknown().optional().describe('Nested trajectory expectation (untyped at this depth)'),\n};\n\n// Typed step variants keyed by stepType\nconst expectedToolCallStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('tool_call'),\n  toolArgs: z.record(z.string(), z.unknown()).optional(),\n  toolResult: z.record(z.string(), z.unknown()).optional(),\n  success: z.boolean().optional(),\n});\n\nconst expectedMcpToolCallStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('mcp_tool_call'),\n  toolArgs: z.record(z.string(), z.unknown()).optional(),\n  toolResult: z.record(z.string(), z.unknown()).optional(),\n  mcpServer: z.string().optional(),\n  success: z.boolean().optional(),\n});\n\nconst expectedModelGenerationStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('model_generation'),\n  modelId: z.string().optional(),\n  promptTokens: z.number().optional(),\n  completionTokens: z.number().optional(),\n  finishReason: z.string().optional(),\n});\n\nconst expectedAgentRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('agent_run'),\n  agentId: z.string().optional(),\n});\n\nconst expectedWorkflowStepStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_step'),\n  stepId: z.string().optional(),\n  status: z.string().optional(),\n  output: z.record(z.string(), z.unknown()).optional(),\n});\n\nconst expectedWorkflowRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_run'),\n  workflowId: z.string().optional(),\n  status: z.string().optional(),\n});\n\nconst expectedWorkflowConditionalStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_conditional'),\n  conditionCount: z.number().optional(),\n  selectedSteps: z.array(z.string()).optional(),\n});\n\nconst expectedWorkflowParallelStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_parallel'),\n  branchCount: z.number().optional(),\n  parallelSteps: z.array(z.string()).optional(),\n});\n\nconst expectedWorkflowLoopStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_loop'),\n  loopType: z.string().optional(),\n  totalIterations: z.number().optional(),\n});\n\nconst expectedWorkflowSleepStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_sleep'),\n  sleepDurationMs: z.number().optional(),\n  sleepType: z.string().optional(),\n});\n\nconst expectedWorkflowWaitEventStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_wait_event'),\n  eventName: z.string().optional(),\n  eventReceived: z.boolean().optional(),\n});\n\nconst expectedProcessorRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('processor_run'),\n  processorId: z.string().optional(),\n});\n\n// Generic step (no stepType — matches by name only)\nconst expectedGenericStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.undefined().optional(),\n});\n\n// Union of all expected step types\nconst expectedStepSchema = z.union([\n  z.discriminatedUnion('stepType', [\n    expectedToolCallStepSchema,\n    expectedMcpToolCallStepSchema,\n    expectedModelGenerationStepSchema,\n    expectedAgentRunStepSchema,\n    expectedWorkflowStepStepSchema,\n    expectedWorkflowRunStepSchema,\n    expectedWorkflowConditionalStepSchema,\n    expectedWorkflowParallelStepSchema,\n    expectedWorkflowLoopStepSchema,\n    expectedWorkflowSleepStepSchema,\n    expectedWorkflowWaitEventStepSchema,\n    expectedProcessorRunStepSchema,\n  ]),\n  expectedGenericStepSchema,\n]);\n\n// Full TrajectoryExpectation schema\nconst trajectoryExpectationSchema = z\n  .object({\n    // Accuracy\n    steps: z.array(expectedStepSchema).optional().describe('Expected steps for accuracy checking'),\n    ordering: z\n      .enum(['strict', 'relaxed', 'unordered'])\n      .optional()\n      .describe('How to compare step ordering (default: relaxed)'),\n    allowRepeatedSteps: z.boolean().optional().describe('Whether to allow repeated steps (default: true)'),\n\n    // Efficiency\n    maxSteps: z.number().int().optional().describe('Maximum number of steps allowed'),\n    maxTotalTokens: z.number().int().optional().describe('Maximum total tokens across all model_generation steps'),\n    maxTotalDurationMs: z.number().optional().describe('Maximum total duration in milliseconds'),\n    noRedundantCalls: z\n      .boolean()\n      .optional()\n      .describe('Whether to penalize redundant calls (same tool + same args consecutively, default: true)'),\n\n    // Blacklist\n    blacklistedTools: z.array(z.string()).optional().describe('Tool names that should never appear'),\n    blacklistedSequences: z\n      .array(z.array(z.string()))\n      .optional()\n      .describe('Tool name sequences that should never appear'),\n\n    // Tool failure tolerance\n    maxRetriesPerTool: z.number().int().optional().describe('Maximum retries per tool before penalizing (default: 2)'),\n  })\n  .optional()\n  .nullable()\n  .describe('Expected trajectory configuration for trajectory scoring');\n\n// Dataset item source tracking\nconst datasetItemSourceSchema = z\n  .object({\n    type: z\n      .enum(['csv', 'json', 'trace', 'llm', 'experiment-result', 'candidate-screener'])\n      .describe('How this item was created'),\n    referenceId: z.string().optional().describe('Reference identifier (e.g., trace id, csv filename)'),\n  })\n  .optional()\n  .describe('Source/provenance of this dataset item');\n\n// Item-level static tool mocks (agent targets only).\nconst itemToolMockSchema = z.object({\n  toolName: z.string().describe('Name of the tool this mock applies to'),\n  args: z.record(z.string(), z.unknown()).describe('Arguments to match against the tool call'),\n  output: z.unknown().describe('Output served to the agent when matched'),\n  matchArgs: z\n    .enum(['strict', 'ignore'])\n    .optional()\n    .describe(\"Argument matching mode. 'strict' (default) deep-equals args; 'ignore' matches on toolName only\"),\n});\n\nconst toolMocksSchema = z\n  .array(itemToolMockSchema)\n  .optional()\n  .describe('Ordered item-level static tool mocks served in place of executing the real tool');\n\nconst unmockedToolPolicySchema = z\n  .enum(['allow', 'deny'])\n  .optional()\n  .describe(\"Policy for undeclared tool calls. 'allow' runs them live; 'deny' fails the experiment item\");\n\n// Diagnostic receipt for item-level tool mocks, persisted on experiment results.\nconst toolMockReportSchema = z\n  .object({\n    served: z.array(z.object({ mockIndex: z.number().int(), toolName: z.string(), args: z.unknown() })),\n    unconsumed: z.array(z.object({ mockIndex: z.number().int(), toolName: z.string(), args: z.unknown() })),\n    liveCalls: z.array(z.object({ toolName: z.string(), args: z.unknown() })),\n    failure: z\n      .object({\n        code: z.enum(['TOOL_MOCK_MISMATCH', 'TOOL_MOCK_EXHAUSTED', 'TOOL_MOCK_NOT_DECLARED']),\n        toolName: z.string(),\n        args: z.unknown(),\n      })\n      .optional(),\n  })\n  .optional()\n  .describe('Diagnostic receipt for item-level tool mocks');\n\n// ============================================================================\n// Path Parameter Schemas\n// ============================================================================\n\nexport const datasetIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n});\n\nexport const experimentIdPathParams = z.object({\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n});\n\nexport const itemIdPathParams = z.object({\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n});\n\nexport const datasetAndExperimentIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n});\n\nexport const experimentResultIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n  resultId: z.string().describe('Unique identifier for the experiment result'),\n});\n\nexport const datasetAndItemIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n});\n\n// ============================================================================\n// Query Parameter Schemas\n// ============================================================================\n\nexport const paginationQuerySchema = z.object({\n  page: z.coerce.number().optional().default(0),\n  perPage: z.coerce.number().optional().default(10),\n});\n\nexport const tenancyQuerySchema = z.object({\n  organizationId: z.string().optional().describe('Restrict lookup to the given organization'),\n  projectId: z.string().optional().describe('Restrict lookup to the given project'),\n});\n\nexport const listItemsQuerySchema = z.object({\n  page: z.coerce.number().optional().default(0),\n  perPage: z.coerce.number().optional().default(10),\n  version: z.coerce.number().int().optional(), // Optional version filter for snapshot semantics\n  search: z.string().optional(),\n});\n\n// ============================================================================\n// Request Body Schemas\n// ============================================================================\n\nexport const createDatasetBodySchema = z.object({\n  name: z.string().describe('Name of the dataset'),\n  description: z.string().optional().describe('Description of the dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const updateDatasetBodySchema = z.object({\n  name: z.string().optional().describe('Name of the dataset'),\n  description: z.string().optional().describe('Description of the dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  tags: z.array(z.string()).optional().describe('Tag definitions for categorizing experiment results'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().nullable().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const addItemBodySchema = z.object({\n  externalId: z.string().optional().nullable().describe('Caller-defined, dataset-local item identity'),\n  input: z.unknown().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  toolMocks: toolMocksSchema,\n  unmockedToolPolicy: unmockedToolPolicySchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const updateItemBodySchema = z.object({\n  input: z.unknown().optional().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  toolMocks: toolMocksSchema,\n  unmockedToolPolicy: unmockedToolPolicySchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const triggerExperimentBodySchema = z.object({\n  targetType: z.enum(['agent', 'workflow', 'scorer']).describe('Type of target to run against'),\n  targetId: z.string().describe('ID of the target'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers to apply'),\n  version: z.coerce.number().int().optional().describe('Pin to specific dataset version'),\n  agentVersion: z.string().optional().describe('Agent version ID to use for experiment'),\n  maxConcurrency: z.number().optional().describe('Maximum concurrent executions'),\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Global request context passed to the target'),\n  versions: z\n    .object({\n      agents: z\n        .record(\n          z.string(),\n          z.union([z.object({ versionId: z.string() }), z.object({ status: z.enum(['draft', 'published']) })]),\n        )\n        .optional(),\n      defaultStatus: z.enum(['draft', 'published']).optional(),\n    })\n    .optional()\n    .describe('Version overrides for sub-agent delegation during experiment execution'),\n});\n\nexport const compareExperimentsBodySchema = z.object({\n  experimentIdA: z.string().describe('ID of baseline experiment'),\n  experimentIdB: z.string().describe('ID of candidate experiment'),\n});\n\n// ============================================================================\n// Response Schemas\n// ============================================================================\n\n// Dataset entity schema\nexport const datasetResponseSchema = z.object({\n  id: z.string(),\n  name: z.string(),\n  description: z.string().optional().nullable(),\n  metadata: z.record(z.string(), z.unknown()).optional().nullable(),\n  inputSchema: z.record(z.string(), z.unknown()).optional(),\n  groundTruthSchema: z.record(z.string(), z.unknown()).optional(),\n  requestContextSchema: z.record(z.string(), z.unknown()).optional(),\n  tags: z.array(z.string()).optional().nullable(),\n  targetType: z.string().optional().nullable(),\n  targetIds: z.array(z.string()).optional().nullable(),\n  scorerIds: z.array(z.string()).optional().nullable(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Dataset item entity schema\nexport const datasetItemResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  externalId: z.string().optional().nullable(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  toolMocks: toolMocksSchema,\n  unmockedToolPolicy: unmockedToolPolicySchema,\n  requestContext: z.record(z.string(), z.unknown()).optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  source: datasetItemSourceSchema,\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Experiment entity schema\nexport const experimentResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string().nullable(),\n  datasetVersion: z.number().int().nullable(),\n  agentVersion: z.string().nullable().optional(),\n  targetType: z.enum(['agent', 'workflow', 'scorer', 'processor']),\n  targetId: z.string(),\n  name: z.string().optional(),\n  description: z.string().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  skippedCount: z.number(),\n  startedAt: z.coerce.date().nullable(),\n  completedAt: z.coerce.date().nullable(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Scorer result schema\nexport const scorerResultSchema = z.object({\n  scorerId: z.string(),\n  scorerName: z.string(),\n  score: z.number().nullable(),\n  reason: z.string().nullable(),\n  error: z.string().nullable(),\n});\n\n// Experiment result entity schema\nexport const experimentResultResponseSchema = z.object({\n  id: z.string(),\n  experimentId: z.string(),\n  itemId: z.string(),\n  itemDatasetVersion: z.number().int().nullable(),\n  input: z.unknown(),\n  output: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  expectedTrajectory: z.unknown().optional(),\n  error: z\n    .object({\n      message: z.string(),\n      stack: z.string().optional(),\n      code: z.string().optional(),\n    })\n    .nullable(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date(),\n  retryCount: z.number(),\n  traceId: z.string().nullable(),\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).nullable().optional(),\n  toolMockReport: toolMockReportSchema.nullable(),\n  createdAt: z.coerce.date(),\n});\n\nexport const updateExperimentResultBodySchema = z.object({\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).optional(),\n});\n\n// Comparison item schema (MVP shape)\nconst comparisonItemSchema = z.object({\n  itemId: z.string(),\n  input: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  results: z.record(\n    z.string(),\n    z\n      .object({\n        output: z.unknown().nullable(),\n        scores: z.record(z.string(), z.number().nullable()),\n      })\n      .nullable(),\n  ),\n});\n\n// Comparison result schema\nexport const comparisonResponseSchema = z.object({\n  baselineId: z.string(),\n  items: z.array(comparisonItemSchema),\n});\n\n// Experiment summary schema (returned by trigger experiment)\n// Note: completedAt is nullable for pending/running experiments (async trigger)\nexport const experimentSummaryResponseSchema = z.object({\n  experimentId: z.string(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date().nullable(),\n  results: z.array(\n    z.object({\n      itemId: z.string(),\n      itemDatasetVersion: z.number().int().nullable(),\n      input: z.unknown(),\n      output: z.unknown().nullable(),\n      groundTruth: z.unknown().nullable(),\n      error: z.string().nullable(),\n      startedAt: z.coerce.date(),\n      completedAt: z.coerce.date(),\n      retryCount: z.number(),\n      toolMockReport: toolMockReportSchema.nullable(),\n      scores: z.array(\n        z.object({\n          scorerId: z.string(),\n          scorerName: z.string(),\n          score: z.number().nullable(),\n          reason: z.string().nullable(),\n          error: z.string().nullable(),\n        }),\n      ),\n    }),\n  ),\n});\n\n// ============================================================================\n// List Response Schemas\n// ============================================================================\n\nexport const listDatasetsResponseSchema = z.object({\n  datasets: z.array(datasetResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentsResponseSchema = z.object({\n  experiments: z.array(experimentResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentResultsResponseSchema = z.object({\n  results: z.array(experimentResultResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const experimentReviewCountsSchema = z.object({\n  experimentId: z.string(),\n  total: z.number().int(),\n  needsReview: z.number().int(),\n  reviewed: z.number().int(),\n  complete: z.number().int(),\n});\n\nexport const reviewSummaryResponseSchema = z.object({\n  counts: z.array(experimentReviewCountsSchema),\n});\n\n// ============================================================================\n// Version Schemas\n// ============================================================================\n\n// Path params for item version routes\nexport const datasetItemVersionPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n  datasetVersion: z.coerce.number().int().describe('Dataset version number'),\n});\n\n// Item history row response schema (SCD-2 DatasetItemRow shape)\nexport const itemVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  toolMocks: toolMocksSchema,\n  unmockedToolPolicy: unmockedToolPolicySchema,\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  validTo: z.number().int().nullable(),\n  isDeleted: z.boolean(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\nexport const listItemVersionsResponseSchema = z.object({\n  history: z.array(itemVersionResponseSchema),\n});\n\n// Dataset version response schema\nexport const datasetVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n});\n\nexport const listDatasetVersionsResponseSchema = z.object({\n  versions: z.array(datasetVersionResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\n// ============================================================================\n// Batch Operation Schemas\n// ============================================================================\n\nexport const batchInsertItemsBodySchema = z.object({\n  items: z.array(\n    z.object({\n      externalId: z.string().optional().nullable(),\n      input: z.unknown(),\n      groundTruth: z.unknown().optional(),\n      expectedTrajectory: trajectoryExpectationSchema,\n      toolMocks: toolMocksSchema,\n      unmockedToolPolicy: unmockedToolPolicySchema,\n      requestContext: z.record(z.string(), z.unknown()).optional(),\n      metadata: z.record(z.string(), z.unknown()).optional(),\n      source: datasetItemSourceSchema,\n    }),\n  ),\n});\n\nexport const batchInsertItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  count: z.number(),\n});\n\nexport const batchDeleteItemsBodySchema = z.object({\n  itemIds: z.array(z.string()),\n});\n\nexport const batchDeleteItemsResponseSchema = z.object({\n  success: z.boolean(),\n  deletedCount: z.number(),\n});\n\n// ============================================================================\n// AI Generation Schemas\n// ============================================================================\n\nexport const generateItemsBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  prompt: z.string().describe('Description of the kind of test data to generate'),\n  count: z.number().int().min(1).max(50).default(5).describe('Number of items to generate'),\n  agentContext: z\n    .object({\n      description: z.string().optional(),\n      instructions: z.string().optional(),\n      tools: z.array(z.string()).optional(),\n    })\n    .optional()\n    .describe('Context about the agent to generate relevant test data'),\n});\n\nconst generatedItemSchema = z.object({\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n});\n\nexport const generateItemsResponseSchema = z.object({\n  items: z.array(generatedItemSchema),\n});\n\n// ============================================================================\n// Cluster Failures\n// ============================================================================\n\nexport const clusterFailuresBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  items: z\n    .array(\n      z.object({\n        id: z.string(),\n        input: z.unknown(),\n        output: z.unknown().optional(),\n        error: z.string().optional(),\n        scores: z.record(z.string(), z.number()).optional(),\n        existingTags: z.array(z.string()).optional().describe('Tags already applied to this item'),\n      }),\n    )\n    .min(1)\n    .max(200)\n    .describe('Failure items to cluster'),\n  availableTags: z\n    .array(z.string())\n    .optional()\n    .describe('Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable.'),\n  prompt: z\n    .string()\n    .optional()\n    .describe('Optional user instructions to guide the analysis (e.g., \"focus on tool usage failures\")'),\n});\n\nconst failureClusterSchema = z.object({\n  id: z.string().describe('A unique cluster identifier'),\n  label: z.string().describe('Short label for this failure pattern'),\n  description: z.string().describe('Description of the common pattern'),\n  itemIds: z.array(z.string()).describe('IDs of items belonging to this cluster'),\n});\n\nexport const clusterFailuresResponseSchema = z.object({\n  clusters: z.array(failureClusterSchema),\n  /** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */\n  proposedTags: z\n    .array(\n      z.object({\n        itemId: z.string(),\n        tags: z.array(z.string()),\n        reason: z.string().describe('Brief explanation of why these tags were assigned to this item'),\n      }),\n    )\n    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dist/datasets-D3kXQaMn.js

+import { s as paginationInfoSchema } from "./common-CPNOpqxA.js";
+import { z } from "zod/v4";
+//#region src/server/schemas/datasets.ts
+const jsonSchemaObject = z.lazy(() => z.record(z.string(), z.unknown()));
+const jsonSchemaField = z.union([jsonSchemaObject, z.null()]).optional();
+const expectedStepBase = {
+	name: z.string().describe("Step name to match"),
+	durationMs: z.number().optional(),
+	metadata: z.record(z.string(), z.unknown()).optional(),
+	children: z.unknown().optional().describe("Nested trajectory expectation (untyped at this depth)")
+};
+const expectedToolCallStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("tool_call"),
+	toolArgs: z.record(z.string(), z.unknown()).optional(),
+	toolResult: z.record(z.string(), z.unknown()).optional(),
+	success: z.boolean().optional()
+});
+const expectedMcpToolCallStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("mcp_tool_call"),
+	toolArgs: z.record(z.string(), z.unknown()).optional(),
+	toolResult: z.record(z.string(), z.unknown()).optional(),
+	mcpServer: z.string().optional(),
+	success: z.boolean().optional()
+});
+const expectedModelGenerationStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("model_generation"),
+	modelId: z.string().optional(),
+	promptTokens: z.number().optional(),
+	completionTokens: z.number().optional(),
+	finishReason: z.string().optional()
+});
+const expectedAgentRunStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("agent_run"),
+	agentId: z.string().optional()
+});
+const expectedWorkflowStepStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("workflow_step"),
+	stepId: z.string().optional(),
+	status: z.string().optional(),
+	output: z.record(z.string(), z.unknown()).optional()
+});
+const expectedWorkflowRunStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("workflow_run"),
+	workflowId: z.string().optional(),
+	status: z.string().optional()
+});
+const expectedWorkflowConditionalStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("workflow_conditional"),
+	conditionCount: z.number().optional(),
+	selectedSteps: z.array(z.string()).optional()
+});
+const expectedWorkflowParallelStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("workflow_parallel"),
+	branchCount: z.number().optional(),
+	parallelSteps: z.array(z.string()).optional()
+});
+const expectedWorkflowLoopStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("workflow_loop"),
+	loopType: z.string().optional(),
+	totalIterations: z.number().optional()
+});
+const expectedWorkflowSleepStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("workflow_sleep"),
+	sleepDurationMs: z.number().optional(),
+	sleepType: z.string().optional()
+});
+const expectedWorkflowWaitEventStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("workflow_wait_event"),
+	eventName: z.string().optional(),
+	eventReceived: z.boolean().optional()
+});
+const expectedProcessorRunStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.literal("processor_run"),
+	processorId: z.string().optional()
+});
+const expectedGenericStepSchema = z.object({
+	...expectedStepBase,
+	stepType: z.undefined().optional()
+});
+const expectedStepSchema = z.union([z.discriminatedUnion("stepType", [
+	expectedToolCallStepSchema,
+	expectedMcpToolCallStepSchema,
+	expectedModelGenerationStepSchema,
+	expectedAgentRunStepSchema,
+	expectedWorkflowStepStepSchema,
+	expectedWorkflowRunStepSchema,
+	expectedWorkflowConditionalStepSchema,
+	expectedWorkflowParallelStepSchema,
+	expectedWorkflowLoopStepSchema,
+	expectedWorkflowSleepStepSchema,
+	expectedWorkflowWaitEventStepSchema,
+	expectedProcessorRunStepSchema
+]), expectedGenericStepSchema]);
+const trajectoryExpectationSchema = z.object({
+	steps: z.array(expectedStepSchema).optional().describe("Expected steps for accuracy checking"),
+	ordering: z.enum([
+		"strict",
+		"relaxed",
+		"unordered"
+	]).optional().describe("How to compare step ordering (default: relaxed)"),
+	allowRepeatedSteps: z.boolean().optional().describe("Whether to allow repeated steps (default: true)"),
+	maxSteps: z.number().int().optional().describe("Maximum number of steps allowed"),
+	maxTotalTokens: z.number().int().optional().describe("Maximum total tokens across all model_generation steps"),
+	maxTotalDurationMs: z.number().optional().describe("Maximum total duration in milliseconds"),
+	noRedundantCalls: z.boolean().optional().describe("Whether to penalize redundant calls (same tool + same args consecutively, default: true)"),
+	blacklistedTools: z.array(z.string()).optional().describe("Tool names that should never appear"),
+	blacklistedSequences: z.array(z.array(z.string())).optional().describe("Tool name sequences that should never appear"),
+	maxRetriesPerTool: z.number().int().optional().describe("Maximum retries per tool before penalizing (default: 2)")
+}).optional().nullable().describe("Expected trajectory configuration for trajectory scoring");
+const datasetItemSourceSchema = z.object({
+	type: z.enum([
+		"csv",
+		"json",
+		"trace",
+		"llm",
+		"experiment-result",
+		"candidate-screener"
+	]).describe("How this item was created"),
+	referenceId: z.string().optional().describe("Reference identifier (e.g., trace id, csv filename)")
+}).optional().describe("Source/provenance of this dataset item");
+const itemToolMockSchema = z.object({
+	toolName: z.string().describe("Name of the tool this mock applies to"),
+	args: z.record(z.string(), z.unknown()).describe("Arguments to match against the tool call"),
+	output: z.unknown().describe("Output served to the agent when matched"),
+	matchArgs: z.enum(["strict", "ignore"]).optional().describe("Argument matching mode. 'strict' (default) deep-equals args; 'ignore' matches on toolName only")
+});
+const toolMocksSchema = z.array(itemToolMockSchema).optional().describe("Ordered item-level static tool mocks served in place of executing the real tool");
+const unmockedToolPolicySchema = z.enum(["allow", "deny"]).optional().describe("Policy for undeclared tool calls. 'allow' runs them live; 'deny' fails the experiment item");
+const toolMockReportSchema = z.object({
+	served: z.array(z.object({
+		mockIndex: z.number().int(),
+		toolName: z.string(),
+		args: z.unknown()
+	})),
+	unconsumed: z.array(z.object({
+		mockIndex: z.number().int(),
+		toolName: z.string(),
+		args: z.unknown()
+	})),
+	liveCalls: z.array(z.object({
+		toolName: z.string(),
+		args: z.unknown()
+	})),
+	failure: z.object({
+		code: z.enum([
+			"TOOL_MOCK_MISMATCH",
+			"TOOL_MOCK_EXHAUSTED",
+			"TOOL_MOCK_NOT_DECLARED"
+		]),
+		toolName: z.string(),
+		args: z.unknown()
+	}).optional()
+}).optional().describe("Diagnostic receipt for item-level tool mocks");
+const datasetIdPathParams = z.object({ datasetId: z.string().describe("Unique identifier for the dataset") });
+const experimentIdPathParams = z.object({ experimentId: z.string().describe("Unique identifier for the experiment") });
+const itemIdPathParams = z.object({ itemId: z.string().describe("Unique identifier for the dataset item") });
+const datasetAndExperimentIdPathParams = z.object({
+	datasetId: z.string().describe("Unique identifier for the dataset"),
+	experimentId: z.string().describe("Unique identifier for the experiment")
+});
+const experimentResultIdPathParams = z.object({
+	datasetId: z.string().describe("Unique identifier for the dataset"),
+	experimentId: z.string().describe("Unique identifier for the experiment"),
+	resultId: z.string().describe("Unique identifier for the experiment result")
+});
+const datasetAndItemIdPathParams = z.object({
+	datasetId: z.string().describe("Unique identifier for the dataset"),
+	itemId: z.string().describe("Unique identifier for the dataset item")
+});
+const paginationQuerySchema = z.object({
+	page: z.coerce.number().optional().default(0),
+	perPage: z.coerce.number().optional().default(10)
+});
+const tenancyQuerySchema = z.object({
+	organizationId: z.string().optional().describe("Restrict lookup to the given organization"),
+	projectId: z.string().optional().describe("Restrict lookup to the given project")
+});
+const listItemsQuerySchema = z.object({
+	page: z.coerce.number().optional().default(0),
+	perPage: z.coerce.number().optional().default(10),
+	version: z.coerce.number().int().optional(),
+	search: z.string().optional()
+});
+const createDatasetBodySchema = z.object({
+	name: z.string().describe("Name of the dataset"),
+	description: z.string().optional().describe("Description of the dataset"),
+	metadata: z.record(z.string(), z.unknown()).optional().describe("Additional metadata"),
+	inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
+	groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
+	requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
+	targetType: z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
+	targetIds: z.array(z.string()).optional().describe("IDs of target entities this dataset is attached to"),
+	scorerIds: z.array(z.string()).optional().describe("IDs of scorers attached to this dataset")
+});
+const updateDatasetBodySchema = z.object({
+	name: z.string().optional().describe("Name of the dataset"),
+	description: z.string().optional().describe("Description of the dataset"),
+	metadata: z.record(z.string(), z.unknown()).optional().describe("Additional metadata"),
+	inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
+	groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
+	requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
+	tags: z.array(z.string()).optional().describe("Tag definitions for categorizing experiment results"),
+	targetType: z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
+	targetIds: z.array(z.string()).optional().describe("IDs of target entities this dataset is attached to"),
+	scorerIds: z.array(z.string()).optional().nullable().describe("IDs of scorers attached to this dataset")
+});
+const addItemBodySchema = z.object({
+	externalId: z.string().optional().nullable().describe("Caller-defined, dataset-local item identity"),
+	input: z.unknown().describe("Input data for the dataset item"),
+	groundTruth: z.unknown().optional().describe("Expected output for comparison"),
+	expectedTrajectory: trajectoryExpectationSchema,
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	requestContext: z.record(z.string(), z.unknown()).optional().describe("Request context preset for this item"),
+	metadata: z.record(z.string(), z.unknown()).optional().describe("Additional metadata"),
+	source: datasetItemSourceSchema
+});
+const updateItemBodySchema = z.object({
+	input: z.unknown().optional().describe("Input data for the dataset item"),
+	groundTruth: z.unknown().optional().describe("Expected output for comparison"),
+	expectedTrajectory: trajectoryExpectationSchema,
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	requestContext: z.record(z.string(), z.unknown()).optional().describe("Request context preset for this item"),
+	metadata: z.record(z.string(), z.unknown()).optional().describe("Additional metadata"),
+	source: datasetItemSourceSchema
+});
+const triggerExperimentBodySchema = z.object({
+	targetType: z.enum([
+		"agent",
+		"workflow",
+		"scorer"
+	]).describe("Type of target to run against"),
+	targetId: z.string().describe("ID of the target"),
+	scorerIds: z.array(z.string()).optional().describe("IDs of scorers to apply"),
+	version: z.coerce.number().int().optional().describe("Pin to specific dataset version"),
+	agentVersion: z.string().optional().describe("Agent version ID to use for experiment"),
+	maxConcurrency: z.number().optional().describe("Maximum concurrent executions"),
+	requestContext: z.record(z.string(), z.unknown()).optional().describe("Global request context passed to the target"),
+	versions: z.object({
+		agents: z.record(z.string(), z.union([z.object({ versionId: z.string() }), z.object({ status: z.enum(["draft", "published"]) })])).optional(),
+		defaultStatus: z.enum(["draft", "published"]).optional()
+	}).optional().describe("Version overrides for sub-agent delegation during experiment execution")
+});
+const compareExperimentsBodySchema = z.object({
+	experimentIdA: z.string().describe("ID of baseline experiment"),
+	experimentIdB: z.string().describe("ID of candidate experiment")
+});
+const datasetResponseSchema = z.object({
+	id: z.string(),
+	name: z.string(),
+	description: z.string().optional().nullable(),
+	metadata: z.record(z.string(), z.unknown()).optional().nullable(),
+	inputSchema: z.record(z.string(), z.unknown()).optional(),
+	groundTruthSchema: z.record(z.string(), z.unknown()).optional(),
+	requestContextSchema: z.record(z.string(), z.unknown()).optional(),
+	tags: z.array(z.string()).optional().nullable(),
+	targetType: z.string().optional().nullable(),
+	targetIds: z.array(z.string()).optional().nullable(),
+	scorerIds: z.array(z.string()).optional().nullable(),
+	version: z.number().int(),
+	createdAt: z.coerce.date(),
+	updatedAt: z.coerce.date()
+});
+const datasetItemResponseSchema = z.object({
+	id: z.string(),
+	datasetId: z.string(),
+	datasetVersion: z.number().int(),
+	externalId: z.string().optional().nullable(),
+	input: z.unknown(),
+	groundTruth: z.unknown().optional(),
+	expectedTrajectory: z.unknown().optional(),
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	requestContext: z.record(z.string(), z.unknown()).optional(),
+	metadata: z.record(z.string(), z.unknown()).optional(),
+	source: datasetItemSourceSchema,
+	createdAt: z.coerce.date(),
+	updatedAt: z.coerce.date()
+});
+const experimentResponseSchema = z.object({
+	id: z.string(),
+	datasetId: z.string().nullable(),
+	datasetVersion: z.number().int().nullable(),
+	agentVersion: z.string().nullable().optional(),
+	targetType: z.enum([
+		"agent",
+		"workflow",
+		"scorer",
+		"processor"
+	]),
+	targetId: z.string(),
+	name: z.string().optional(),
+	description: z.string().optional(),
+	metadata: z.record(z.string(), z.unknown()).optional(),
+	status: z.enum([
+		"pending",
+		"running",
+		"completed",
+		"failed"
+	]),
+	totalItems: z.number(),
+	succeededCount: z.number(),
+	failedCount: z.number(),
+	skippedCount: z.number(),
+	startedAt: z.coerce.date().nullable(),
+	completedAt: z.coerce.date().nullable(),
+	createdAt: z.coerce.date(),
+	updatedAt: z.coerce.date()
+});
+const scorerResultSchema = z.object({
+	scorerId: z.string(),
+	scorerName: z.string(),
+	score: z.number().nullable(),
+	reason: z.string().nullable(),
+	error: z.string().nullable()
+});
+const experimentResultResponseSchema = z.object({
+	id: z.string(),
+	experimentId: z.string(),
+	itemId: z.string(),
+	itemDatasetVersion: z.number().int().nullable(),
+	input: z.unknown(),
+	output: z.unknown().nullable(),
+	groundTruth: z.unknown().nullable(),
+	expectedTrajectory: z.unknown().optional(),
+	error: z.object({
+		message: z.string(),
+		stack: z.string().optional(),
+		code: z.string().optional()
+	}).nullable(),
+	startedAt: z.coerce.date(),
+	completedAt: z.coerce.date(),
+	retryCount: z.number(),
+	traceId: z.string().nullable(),
+	status: z.enum([
+		"needs-review",
+		"reviewed",
+		"complete"
+	]).nullable().optional(),
+	tags: z.array(z.string()).nullable().optional(),
+	toolMockReport: toolMockReportSchema.nullable(),
+	createdAt: z.coerce.date()
+});
+const updateExperimentResultBodySchema = z.object({
+	status: z.enum([
+		"needs-review",
+		"reviewed",
+		"complete"
+	]).nullable().optional(),
+	tags: z.array(z.string()).optional()
+});
+const comparisonItemSchema = z.object({
+	itemId: z.string(),
+	input: z.unknown().nullable(),
+	groundTruth: z.unknown().nullable(),
+	results: z.record(z.string(), z.object({
+		output: z.unknown().nullable(),
+		scores: z.record(z.string(), z.number().nullable())
+	}).nullable())
+});
+const comparisonResponseSchema = z.object({
+	baselineId: z.string(),
+	items: z.array(comparisonItemSchema)
+});
+const experimentSummaryResponseSchema = z.object({
+	experimentId: z.string(),
+	status: z.enum([
+		"pending",
+		"running",
+		"completed",
+		"failed"
+	]),
+	totalItems: z.number(),
+	succeededCount: z.number(),
+	failedCount: z.number(),
+	startedAt: z.coerce.date(),
+	completedAt: z.coerce.date().nullable(),
+	results: z.array(z.object({
+		itemId: z.string(),
+		itemDatasetVersion: z.number().int().nullable(),
+		input: z.unknown(),
+		output: z.unknown().nullable(),
+		groundTruth: z.unknown().nullable(),
+		error: z.string().nullable(),
+		startedAt: z.coerce.date(),
+		completedAt: z.coerce.date(),
+		retryCount: z.number(),
+		toolMockReport: toolMockReportSchema.nullable(),
+		scores: z.array(z.object({
+			scorerId: z.string(),
+			scorerName: z.string(),
+			score: z.number().nullable(),
+			reason: z.string().nullable(),
+			error: z.string().nullable()
+		}))
+	}))
+});
+const listDatasetsResponseSchema = z.object({
+	datasets: z.array(datasetResponseSchema),
+	pagination: paginationInfoSchema
+});
+const listItemsResponseSchema = z.object({
+	items: z.array(datasetItemResponseSchema),
+	pagination: paginationInfoSchema
+});
+const listExperimentsResponseSchema = z.object({
+	experiments: z.array(experimentResponseSchema),
+	pagination: paginationInfoSchema
+});
+const listExperimentResultsResponseSchema = z.object({
+	results: z.array(experimentResultResponseSchema),
+	pagination: paginationInfoSchema
+});
+const experimentReviewCountsSchema = z.object({
+	experimentId: z.string(),
+	total: z.number().int(),
+	needsReview: z.number().int(),
+	reviewed: z.number().int(),
+	complete: z.number().int()
+});
+const reviewSummaryResponseSchema = z.object({ counts: z.array(experimentReviewCountsSchema) });
+const datasetItemVersionPathParams = z.object({
+	datasetId: z.string().describe("Unique identifier for the dataset"),
+	itemId: z.string().describe("Unique identifier for the dataset item"),
+	datasetVersion: z.coerce.number().int().describe("Dataset version number")
+});
+const itemVersionResponseSchema = z.object({
+	id: z.string(),
+	datasetId: z.string(),
+	datasetVersion: z.number().int(),
+	input: z.unknown(),
+	groundTruth: z.unknown().optional(),
+	expectedTrajectory: z.unknown().optional(),
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	metadata: z.record(z.string(), z.unknown()).optional(),
+	validTo: z.number().int().nullable(),
+	isDeleted: z.boolean(),
+	createdAt: z.coerce.date(),
+	updatedAt: z.coerce.date()
+});
+const listItemVersionsResponseSchema = z.object({ history: z.array(itemVersionResponseSchema) });
+const datasetVersionResponseSchema = z.object({
+	id: z.string(),
+	datasetId: z.string(),
+	version: z.number().int(),
+	createdAt: z.coerce.date()
+});
+const listDatasetVersionsResponseSchema = z.object({
+	versions: z.array(datasetVersionResponseSchema),
+	pagination: paginationInfoSchema
+});
+const batchInsertItemsBodySchema = z.object({ items: z.array(z.object({
+	externalId: z.string().optional().nullable(),
+	input: z.unknown(),
+	groundTruth: z.unknown().optional(),
+	expectedTrajectory: trajectoryExpectationSchema,
+	toolMocks: toolMocksSchema,
+	unmockedToolPolicy: unmockedToolPolicySchema,
+	requestContext: z.record(z.string(), z.unknown()).optional(),
+	metadata: z.record(z.string(), z.unknown()).optional(),
+	source: datasetItemSourceSchema
+})) });
+const batchInsertItemsResponseSchema = z.object({
+	items: z.array(datasetItemResponseSchema),
+	count: z.number()
+});
+const batchDeleteItemsBodySchema = z.object({ itemIds: z.array(z.string()) });
+const batchDeleteItemsResponseSchema = z.object({
+	success: z.boolean(),
+	deletedCount: z.number()
+});
+const generateItemsBodySchema = z.object({
+	modelId: z.string().describe("Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")"),
+	prompt: z.string().describe("Description of the kind of test data to generate"),
+	count: z.number().int().min(1).max(50).default(5).describe("Number of items to generate"),
+	agentContext: z.object({
+		description: z.string().optional(),
+		instructions: z.string().optional(),
+		tools: z.array(z.string()).optional()
+	}).optional().describe("Context about the agent to generate relevant test data")
+});
+const generatedItemSchema = z.object({
+	input: z.unknown(),
+	groundTruth: z.unknown().optional()
+});
+const generateItemsResponseSchema = z.object({ items: z.array(generatedItemSchema) });
+const clusterFailuresBodySchema = z.object({
+	modelId: z.string().describe("Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")"),
+	items: z.array(z.object({
+		id: z.string(),
+		input: z.unknown(),
+		output: z.unknown().optional(),
+		error: z.string().optional(),
+		scores: z.record(z.string(), z.number()).optional(),
+		existingTags: z.array(z.string()).optional().describe("Tags already applied to this item")
+	})).min(1).max(200).describe("Failure items to cluster"),
+	availableTags: z.array(z.string()).optional().describe("Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable."),
+	prompt: z.string().optional().describe("Optional user instructions to guide the analysis (e.g., \"focus on tool usage failures\")")
+});
+const failureClusterSchema = z.object({
+	id: z.string().describe("A unique cluster identifier"),
+	label: z.string().describe("Short label for this failure pattern"),
+	description: z.string().describe("Description of the common pattern"),
+	itemIds: z.array(z.string()).describe("IDs of items belonging to this cluster")
+});
+const clusterFailuresResponseSchema = z.object({
+	clusters: z.array(failureClusterSchema),
+	/** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */
+	proposedTags: z.array(z.object({
+		itemId: z.string(),
+		tags: z.array(z.string()),
+		reason: z.string().describe("Brief explanation of why these tags were assigned to this item")
+	})).optional()
+});
+//#endregion
+export { listExperimentResultsResponseSchema as A, updateDatasetBodySchema as B, experimentSummaryResponseSchema as C, itemVersionResponseSchema as D, itemIdPathParams as E, paginationQuerySchema as F, updateItemBodySchema as H, reviewSummaryResponseSchema as I, scorerResultSchema as L, listItemVersionsResponseSchema as M, listItemsQuerySchema as N, listDatasetVersionsResponseSchema as O, listItemsResponseSchema as P, tenancyQuerySchema as R, experimentReviewCountsSchema as S, generateItemsResponseSchema as T, updateExperimentResultBodySchema as V, datasetVersionResponseSchema as _, batchInsertItemsResponseSchema as a, experimentResultIdPathParams as b, compareExperimentsBodySchema as c, datasetAndExperimentIdPathParams as d, datasetAndItemIdPathParams as f, datasetResponseSchema as g, datasetItemVersionPathParams as h, batchInsertItemsBodySchema as i, listExperimentsResponseSchema as j, listDatasetsResponseSchema as k, comparisonResponseSchema as l, datasetItemResponseSchema as m, batchDeleteItemsBodySchema as n, clusterFailuresBodySchema as o, datasetIdPathParams as p, batchDeleteItemsResponseSchema as r, clusterFailuresResponseSchema as s, addItemBodySchema as t, createDatasetBodySchema as u, experimentIdPathParams as v, generateItemsBodySchema as w, experimentResultResponseSchema as x, experimentResponseSchema as y, triggerExperimentBodySchema as z };
+
+//# sourceMappingURL=datasets-D3kXQaMn.js.map

dist/datasets-D3kXQaMn.js.map

+{"version":3,"file":"datasets-D3kXQaMn.js","names":[],"sources":["../src/server/schemas/datasets.ts"],"sourcesContent":["import { z } from 'zod/v4';\nimport { paginationInfoSchema } from './common';\n\n// ============================================================================\n// JSON Schema Types (for inputSchema/groundTruthSchema fields)\n// ============================================================================\n\n// JSON Schema type (simplified for storage - full spec too complex)\nconst jsonSchemaObject: z.ZodType<Record<string, unknown>> = z.lazy(() => z.record(z.string(), z.unknown()));\n\n// JSON Schema field (object or null to disable)\nconst jsonSchemaField = z.union([jsonSchemaObject, z.null()]).optional();\n\n// ============================================================================\n// Trajectory Expectation Schema (2 levels deep, children at level 2 use z.unknown())\n// ============================================================================\n\n// Shared base fields for expected steps (level 2 — children typed as z.unknown())\nconst expectedStepBase = {\n  name: z.string().describe('Step name to match'),\n  durationMs: z.number().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  children: z.unknown().optional().describe('Nested trajectory expectation (untyped at this depth)'),\n};\n\n// Typed step variants keyed by stepType\nconst expectedToolCallStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('tool_call'),\n  toolArgs: z.record(z.string(), z.unknown()).optional(),\n  toolResult: z.record(z.string(), z.unknown()).optional(),\n  success: z.boolean().optional(),\n});\n\nconst expectedMcpToolCallStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('mcp_tool_call'),\n  toolArgs: z.record(z.string(), z.unknown()).optional(),\n  toolResult: z.record(z.string(), z.unknown()).optional(),\n  mcpServer: z.string().optional(),\n  success: z.boolean().optional(),\n});\n\nconst expectedModelGenerationStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('model_generation'),\n  modelId: z.string().optional(),\n  promptTokens: z.number().optional(),\n  completionTokens: z.number().optional(),\n  finishReason: z.string().optional(),\n});\n\nconst expectedAgentRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('agent_run'),\n  agentId: z.string().optional(),\n});\n\nconst expectedWorkflowStepStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_step'),\n  stepId: z.string().optional(),\n  status: z.string().optional(),\n  output: z.record(z.string(), z.unknown()).optional(),\n});\n\nconst expectedWorkflowRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_run'),\n  workflowId: z.string().optional(),\n  status: z.string().optional(),\n});\n\nconst expectedWorkflowConditionalStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_conditional'),\n  conditionCount: z.number().optional(),\n  selectedSteps: z.array(z.string()).optional(),\n});\n\nconst expectedWorkflowParallelStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_parallel'),\n  branchCount: z.number().optional(),\n  parallelSteps: z.array(z.string()).optional(),\n});\n\nconst expectedWorkflowLoopStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_loop'),\n  loopType: z.string().optional(),\n  totalIterations: z.number().optional(),\n});\n\nconst expectedWorkflowSleepStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_sleep'),\n  sleepDurationMs: z.number().optional(),\n  sleepType: z.string().optional(),\n});\n\nconst expectedWorkflowWaitEventStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_wait_event'),\n  eventName: z.string().optional(),\n  eventReceived: z.boolean().optional(),\n});\n\nconst expectedProcessorRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('processor_run'),\n  processorId: z.string().optional(),\n});\n\n// Generic step (no stepType — matches by name only)\nconst expectedGenericStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.undefined().optional(),\n});\n\n// Union of all expected step types\nconst expectedStepSchema = z.union([\n  z.discriminatedUnion('stepType', [\n    expectedToolCallStepSchema,\n    expectedMcpToolCallStepSchema,\n    expectedModelGenerationStepSchema,\n    expectedAgentRunStepSchema,\n    expectedWorkflowStepStepSchema,\n    expectedWorkflowRunStepSchema,\n    expectedWorkflowConditionalStepSchema,\n    expectedWorkflowParallelStepSchema,\n    expectedWorkflowLoopStepSchema,\n    expectedWorkflowSleepStepSchema,\n    expectedWorkflowWaitEventStepSchema,\n    expectedProcessorRunStepSchema,\n  ]),\n  expectedGenericStepSchema,\n]);\n\n// Full TrajectoryExpectation schema\nconst trajectoryExpectationSchema = z\n  .object({\n    // Accuracy\n    steps: z.array(expectedStepSchema).optional().describe('Expected steps for accuracy checking'),\n    ordering: z\n      .enum(['strict', 'relaxed', 'unordered'])\n      .optional()\n      .describe('How to compare step ordering (default: relaxed)'),\n    allowRepeatedSteps: z.boolean().optional().describe('Whether to allow repeated steps (default: true)'),\n\n    // Efficiency\n    maxSteps: z.number().int().optional().describe('Maximum number of steps allowed'),\n    maxTotalTokens: z.number().int().optional().describe('Maximum total tokens across all model_generation steps'),\n    maxTotalDurationMs: z.number().optional().describe('Maximum total duration in milliseconds'),\n    noRedundantCalls: z\n      .boolean()\n      .optional()\n      .describe('Whether to penalize redundant calls (same tool + same args consecutively, default: true)'),\n\n    // Blacklist\n    blacklistedTools: z.array(z.string()).optional().describe('Tool names that should never appear'),\n    blacklistedSequences: z\n      .array(z.array(z.string()))\n      .optional()\n      .describe('Tool name sequences that should never appear'),\n\n    // Tool failure tolerance\n    maxRetriesPerTool: z.number().int().optional().describe('Maximum retries per tool before penalizing (default: 2)'),\n  })\n  .optional()\n  .nullable()\n  .describe('Expected trajectory configuration for trajectory scoring');\n\n// Dataset item source tracking\nconst datasetItemSourceSchema = z\n  .object({\n    type: z\n      .enum(['csv', 'json', 'trace', 'llm', 'experiment-result', 'candidate-screener'])\n      .describe('How this item was created'),\n    referenceId: z.string().optional().describe('Reference identifier (e.g., trace id, csv filename)'),\n  })\n  .optional()\n  .describe('Source/provenance of this dataset item');\n\n// Item-level static tool mocks (agent targets only).\nconst itemToolMockSchema = z.object({\n  toolName: z.string().describe('Name of the tool this mock applies to'),\n  args: z.record(z.string(), z.unknown()).describe('Arguments to match against the tool call'),\n  output: z.unknown().describe('Output served to the agent when matched'),\n  matchArgs: z\n    .enum(['strict', 'ignore'])\n    .optional()\n    .describe(\"Argument matching mode. 'strict' (default) deep-equals args; 'ignore' matches on toolName only\"),\n});\n\nconst toolMocksSchema = z\n  .array(itemToolMockSchema)\n  .optional()\n  .describe('Ordered item-level static tool mocks served in place of executing the real tool');\n\nconst unmockedToolPolicySchema = z\n  .enum(['allow', 'deny'])\n  .optional()\n  .describe(\"Policy for undeclared tool calls. 'allow' runs them live; 'deny' fails the experiment item\");\n\n// Diagnostic receipt for item-level tool mocks, persisted on experiment results.\nconst toolMockReportSchema = z\n  .object({\n    served: z.array(z.object({ mockIndex: z.number().int(), toolName: z.string(), args: z.unknown() })),\n    unconsumed: z.array(z.object({ mockIndex: z.number().int(), toolName: z.string(), args: z.unknown() })),\n    liveCalls: z.array(z.object({ toolName: z.string(), args: z.unknown() })),\n    failure: z\n      .object({\n        code: z.enum(['TOOL_MOCK_MISMATCH', 'TOOL_MOCK_EXHAUSTED', 'TOOL_MOCK_NOT_DECLARED']),\n        toolName: z.string(),\n        args: z.unknown(),\n      })\n      .optional(),\n  })\n  .optional()\n  .describe('Diagnostic receipt for item-level tool mocks');\n\n// ============================================================================\n// Path Parameter Schemas\n// ============================================================================\n\nexport const datasetIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n});\n\nexport const experimentIdPathParams = z.object({\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n});\n\nexport const itemIdPathParams = z.object({\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n});\n\nexport const datasetAndExperimentIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n});\n\nexport const experimentResultIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n  resultId: z.string().describe('Unique identifier for the experiment result'),\n});\n\nexport const datasetAndItemIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n});\n\n// ============================================================================\n// Query Parameter Schemas\n// ============================================================================\n\nexport const paginationQuerySchema = z.object({\n  page: z.coerce.number().optional().default(0),\n  perPage: z.coerce.number().optional().default(10),\n});\n\nexport const tenancyQuerySchema = z.object({\n  organizationId: z.string().optional().describe('Restrict lookup to the given organization'),\n  projectId: z.string().optional().describe('Restrict lookup to the given project'),\n});\n\nexport const listItemsQuerySchema = z.object({\n  page: z.coerce.number().optional().default(0),\n  perPage: z.coerce.number().optional().default(10),\n  version: z.coerce.number().int().optional(), // Optional version filter for snapshot semantics\n  search: z.string().optional(),\n});\n\n// ============================================================================\n// Request Body Schemas\n// ============================================================================\n\nexport const createDatasetBodySchema = z.object({\n  name: z.string().describe('Name of the dataset'),\n  description: z.string().optional().describe('Description of the dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const updateDatasetBodySchema = z.object({\n  name: z.string().optional().describe('Name of the dataset'),\n  description: z.string().optional().describe('Description of the dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  tags: z.array(z.string()).optional().describe('Tag definitions for categorizing experiment results'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().nullable().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const addItemBodySchema = z.object({\n  externalId: z.string().optional().nullable().describe('Caller-defined, dataset-local item identity'),\n  input: z.unknown().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  toolMocks: toolMocksSchema,\n  unmockedToolPolicy: unmockedToolPolicySchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const updateItemBodySchema = z.object({\n  input: z.unknown().optional().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  toolMocks: toolMocksSchema,\n  unmockedToolPolicy: unmockedToolPolicySchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const triggerExperimentBodySchema = z.object({\n  targetType: z.enum(['agent', 'workflow', 'scorer']).describe('Type of target to run against'),\n  targetId: z.string().describe('ID of the target'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers to apply'),\n  version: z.coerce.number().int().optional().describe('Pin to specific dataset version'),\n  agentVersion: z.string().optional().describe('Agent version ID to use for experiment'),\n  maxConcurrency: z.number().optional().describe('Maximum concurrent executions'),\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Global request context passed to the target'),\n  versions: z\n    .object({\n      agents: z\n        .record(\n          z.string(),\n          z.union([z.object({ versionId: z.string() }), z.object({ status: z.enum(['draft', 'published']) })]),\n        )\n        .optional(),\n      defaultStatus: z.enum(['draft', 'published']).optional(),\n    })\n    .optional()\n    .describe('Version overrides for sub-agent delegation during experiment execution'),\n});\n\nexport const compareExperimentsBodySchema = z.object({\n  experimentIdA: z.string().describe('ID of baseline experiment'),\n  experimentIdB: z.string().describe('ID of candidate experiment'),\n});\n\n// ============================================================================\n// Response Schemas\n// ============================================================================\n\n// Dataset entity schema\nexport const datasetResponseSchema = z.object({\n  id: z.string(),\n  name: z.string(),\n  description: z.string().optional().nullable(),\n  metadata: z.record(z.string(), z.unknown()).optional().nullable(),\n  inputSchema: z.record(z.string(), z.unknown()).optional(),\n  groundTruthSchema: z.record(z.string(), z.unknown()).optional(),\n  requestContextSchema: z.record(z.string(), z.unknown()).optional(),\n  tags: z.array(z.string()).optional().nullable(),\n  targetType: z.string().optional().nullable(),\n  targetIds: z.array(z.string()).optional().nullable(),\n  scorerIds: z.array(z.string()).optional().nullable(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Dataset item entity schema\nexport const datasetItemResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  externalId: z.string().optional().nullable(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  toolMocks: toolMocksSchema,\n  unmockedToolPolicy: unmockedToolPolicySchema,\n  requestContext: z.record(z.string(), z.unknown()).optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  source: datasetItemSourceSchema,\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Experiment entity schema\nexport const experimentResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string().nullable(),\n  datasetVersion: z.number().int().nullable(),\n  agentVersion: z.string().nullable().optional(),\n  targetType: z.enum(['agent', 'workflow', 'scorer', 'processor']),\n  targetId: z.string(),\n  name: z.string().optional(),\n  description: z.string().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  skippedCount: z.number(),\n  startedAt: z.coerce.date().nullable(),\n  completedAt: z.coerce.date().nullable(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Scorer result schema\nexport const scorerResultSchema = z.object({\n  scorerId: z.string(),\n  scorerName: z.string(),\n  score: z.number().nullable(),\n  reason: z.string().nullable(),\n  error: z.string().nullable(),\n});\n\n// Experiment result entity schema\nexport const experimentResultResponseSchema = z.object({\n  id: z.string(),\n  experimentId: z.string(),\n  itemId: z.string(),\n  itemDatasetVersion: z.number().int().nullable(),\n  input: z.unknown(),\n  output: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  expectedTrajectory: z.unknown().optional(),\n  error: z\n    .object({\n      message: z.string(),\n      stack: z.string().optional(),\n      code: z.string().optional(),\n    })\n    .nullable(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date(),\n  retryCount: z.number(),\n  traceId: z.string().nullable(),\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).nullable().optional(),\n  toolMockReport: toolMockReportSchema.nullable(),\n  createdAt: z.coerce.date(),\n});\n\nexport const updateExperimentResultBodySchema = z.object({\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).optional(),\n});\n\n// Comparison item schema (MVP shape)\nconst comparisonItemSchema = z.object({\n  itemId: z.string(),\n  input: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  results: z.record(\n    z.string(),\n    z\n      .object({\n        output: z.unknown().nullable(),\n        scores: z.record(z.string(), z.number().nullable()),\n      })\n      .nullable(),\n  ),\n});\n\n// Comparison result schema\nexport const comparisonResponseSchema = z.object({\n  baselineId: z.string(),\n  items: z.array(comparisonItemSchema),\n});\n\n// Experiment summary schema (returned by trigger experiment)\n// Note: completedAt is nullable for pending/running experiments (async trigger)\nexport const experimentSummaryResponseSchema = z.object({\n  experimentId: z.string(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date().nullable(),\n  results: z.array(\n    z.object({\n      itemId: z.string(),\n      itemDatasetVersion: z.number().int().nullable(),\n      input: z.unknown(),\n      output: z.unknown().nullable(),\n      groundTruth: z.unknown().nullable(),\n      error: z.string().nullable(),\n      startedAt: z.coerce.date(),\n      completedAt: z.coerce.date(),\n      retryCount: z.number(),\n      toolMockReport: toolMockReportSchema.nullable(),\n      scores: z.array(\n        z.object({\n          scorerId: z.string(),\n          scorerName: z.string(),\n          score: z.number().nullable(),\n          reason: z.string().nullable(),\n          error: z.string().nullable(),\n        }),\n      ),\n    }),\n  ),\n});\n\n// ============================================================================\n// List Response Schemas\n// ============================================================================\n\nexport const listDatasetsResponseSchema = z.object({\n  datasets: z.array(datasetResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentsResponseSchema = z.object({\n  experiments: z.array(experimentResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentResultsResponseSchema = z.object({\n  results: z.array(experimentResultResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const experimentReviewCountsSchema = z.object({\n  experimentId: z.string(),\n  total: z.number().int(),\n  needsReview: z.number().int(),\n  reviewed: z.number().int(),\n  complete: z.number().int(),\n});\n\nexport const reviewSummaryResponseSchema = z.object({\n  counts: z.array(experimentReviewCountsSchema),\n});\n\n// ============================================================================\n// Version Schemas\n// ============================================================================\n\n// Path params for item version routes\nexport const datasetItemVersionPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n  datasetVersion: z.coerce.number().int().describe('Dataset version number'),\n});\n\n// Item history row response schema (SCD-2 DatasetItemRow shape)\nexport const itemVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  toolMocks: toolMocksSchema,\n  unmockedToolPolicy: unmockedToolPolicySchema,\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  validTo: z.number().int().nullable(),\n  isDeleted: z.boolean(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\nexport const listItemVersionsResponseSchema = z.object({\n  history: z.array(itemVersionResponseSchema),\n});\n\n// Dataset version response schema\nexport const datasetVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n});\n\nexport const listDatasetVersionsResponseSchema = z.object({\n  versions: z.array(datasetVersionResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\n// ============================================================================\n// Batch Operation Schemas\n// ============================================================================\n\nexport const batchInsertItemsBodySchema = z.object({\n  items: z.array(\n    z.object({\n      externalId: z.string().optional().nullable(),\n      input: z.unknown(),\n      groundTruth: z.unknown().optional(),\n      expectedTrajectory: trajectoryExpectationSchema,\n      toolMocks: toolMocksSchema,\n      unmockedToolPolicy: unmockedToolPolicySchema,\n      requestContext: z.record(z.string(), z.unknown()).optional(),\n      metadata: z.record(z.string(), z.unknown()).optional(),\n      source: datasetItemSourceSchema,\n    }),\n  ),\n});\n\nexport const batchInsertItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  count: z.number(),\n});\n\nexport const batchDeleteItemsBodySchema = z.object({\n  itemIds: z.array(z.string()),\n});\n\nexport const batchDeleteItemsResponseSchema = z.object({\n  success: z.boolean(),\n  deletedCount: z.number(),\n});\n\n// ============================================================================\n// AI Generation Schemas\n// ============================================================================\n\nexport const generateItemsBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  prompt: z.string().describe('Description of the kind of test data to generate'),\n  count: z.number().int().min(1).max(50).default(5).describe('Number of items to generate'),\n  agentContext: z\n    .object({\n      description: z.string().optional(),\n      instructions: z.string().optional(),\n      tools: z.array(z.string()).optional(),\n    })\n    .optional()\n    .describe('Context about the agent to generate relevant test data'),\n});\n\nconst generatedItemSchema = z.object({\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n});\n\nexport const generateItemsResponseSchema = z.object({\n  items: z.array(generatedItemSchema),\n});\n\n// ============================================================================\n// Cluster Failures\n// ============================================================================\n\nexport const clusterFailuresBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  items: z\n    .array(\n      z.object({\n        id: z.string(),\n        input: z.unknown(),\n        output: z.unknown().optional(),\n        error: z.string().optional(),\n        scores: z.record(z.string(), z.number()).optional(),\n        existingTags: z.array(z.string()).optional().describe('Tags already applied to this item'),\n      }),\n    )\n    .min(1)\n    .max(200)\n    .describe('Failure items to cluster'),\n  availableTags: z\n    .array(z.string())\n    .optional()\n    .describe('Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable.'),\n  prompt: z\n    .string()\n    .optional()\n    .describe('Optional user instructions to guide the analysis (e.g., \"focus on tool usage failures\")'),\n});\n\nconst failureClusterSchema = z.object({\n  id: z.string().describe('A unique cluster identifier'),\n  label: z.string().describe('Short label for this failure pattern'),\n  description: z.string().describe('Description of the common pattern'),\n  itemIds: z.array(z.string()).describe('IDs of items belonging to this cluster'),\n});\n\nexport const clusterFailuresResponseSchema = z.object({\n  clusters: z.array(failureClusterSchema),\n  /** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */\n  proposedTags: z\n    .array(\n      z.object({\n        itemId: z.string(),\n        tags: z.array(z.string()),\n        reason: z.string().describe('Brief explanation of why these tags were assigned to this item'),\n      }),\n    )\n    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dist/docs/assets/SOURCE_MAP.json

 {
-  "version": "1.55.0",
+  "version": "1.56.0-alpha.0",
   "package": "@mastra/server",
@@ -4,0 +4,0 @@ "exports": {},

dist/docs/SKILL.md

@@ -6,3 +6,3 @@ ---
   package: "@mastra/server"
-  version: "1.55.0"
+  version: "1.56.0-alpha.0"
 ---
@@ -9,0 +9,0 @@

dist/server/handlers/datasets.cjs

@@ -7,3 +7,3 @@ Object.defineProperty(exports, Symbol.toStringTag, { value: "Module" });
 const require_server_handlers_error = require("./error.cjs");
-const require_datasets = require("../../datasets-BnzmEeJy.cjs");
+const require_datasets = require("../../datasets-BGbh4uCo.cjs");
 let zod = require("zod");
@@ -267,3 +267,3 @@ let _mastra_core_agent = require("@mastra/core/agent");
 		try {
-			const { externalId, input, groundTruth, requestContext, metadata, source, expectedTrajectory, toolMocks } = params;
+			const { externalId, input, groundTruth, requestContext, metadata, source, expectedTrajectory, toolMocks, unmockedToolPolicy } = params;
 			return await (await mastra.datasets.get({ id: datasetId })).addItem({
@@ -277,3 +277,4 @@ externalId: externalId ?? void 0,
 				expectedTrajectory,
-				toolMocks
+				toolMocks,
+				unmockedToolPolicy
 			});
@@ -339,3 +340,3 @@ } catch (error) {
 		try {
-			const { input, groundTruth, requestContext, metadata, expectedTrajectory, toolMocks } = params;
+			const { input, groundTruth, requestContext, metadata, expectedTrajectory, toolMocks, unmockedToolPolicy } = params;
 			const ds = await mastra.datasets.get({ id: datasetId });
@@ -351,3 +352,4 @@ const existing = await ds.getItem({ itemId });
 				expectedTrajectory,
-				toolMocks
+				toolMocks,
+				unmockedToolPolicy
 			});
@@ -354,0 +356,0 @@ } catch (error) {

dist/server/handlers/datasets.d.ts.map

@@ -1,1 +0,1 @@
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+{"version":3,"file":"datasets.d.ts","sourceRoot":"","sources":["../../../src/server/handlers/datasets.ts"],"names":[],"mappings":"AAMA,OAAO,EAAE,CAAC,EAAE,MAAM,KAAK,CAAC;AAwHxB,eAAO,MAAM,mBAAmB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;uCA0B9B,CAAC;AAEH,eAAO,MAAM,oBAAoB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;wCAsD/B,CAAC;AAEH,eAAO,MAAM,iBAAiB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;mDAwB5B,CAAC;AAEH,eAAO,MAAM,oBAAoB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;oDA2E/B,CAAC;AAEH,eAAO,MAAM,oBAAoB;;;;;;;;;;;;;;qDA+B/B,CAAC;AAMH,eAAO,MAAM,gBAAgB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;wDAmC3B,CAAC;AAEH,eAAO,MAAM,cAAc;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;yDAqEzB,CAAC;AAEH,eAAO,MAAM,cAAc;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iEA0BzB,CAAC;AAEH,eAAO,MAAM,iBAAiB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;kEAqD5B,CAAC;AAEH,eAAO,MAAM,iBAAiB;;;;;;;;;;mEA2B5B,CAAC;AAMH,eAAO,MAAM,0BAA0B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;0CAiCrC,CAAC;AAEH,eAAO,MAAM,+BAA+B;;;;;;;;;;;;;;;;yDA6B1C,CAAC;AAEH,eAAO,MAAM,sBAAsB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;8DAyBjC,CAAC;AAEH,eAAO,MAAM,wBAAwB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;+DAkEnC,CAAC;AAEH,eAAO,MAAM,oBAAoB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;6EA0B/B,CAAC;AAEH,eAAO,MAAM,6BAA6B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;oFAiCxC,CAAC;AAEH,eAAO,MAAM,8BAA8B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;gGAsCzC,CAAC;AAMH,eAAO,MAAM,yBAAyB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;2DAgCpC,CAAC;AAMH,eAAO,MAAM,2BAA2B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;2DAyBtC,CAAC;AAEH,eAAO,MAAM,wBAAwB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;wEA2BnC,CAAC;AAEH,eAAO,MAAM,sBAAsB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;0FA6BjC,CAAC;AAMH,eAAO,MAAM,wBAAwB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;+DAqDnC,CAAC;AAEH,eAAO,MAAM,wBAAwB;;;;;;;;;;;;;;iEAyBnC,CAAC;AAoBH,eAAO,MAAM,oBAAoB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;kEA+G/B,CAAC;AAyBH,eAAO,MAAM,sBAAsB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;yDAkFjC,CAAC"}

dist/server/handlers/datasets.js

@@ -6,3 +6,3 @@ import { t as HTTPException } from "../../http-exception-CyfxZB6W.js";
 import { handleError } from "./error.js";
-import { A as listExperimentResultsResponseSchema, B as updateDatasetBodySchema, C as experimentSummaryResponseSchema, F as paginationQuerySchema, H as updateItemBodySchema, I as reviewSummaryResponseSchema, M as listItemVersionsResponseSchema, N as listItemsQuerySchema, O as listDatasetVersionsResponseSchema, P as listItemsResponseSchema, R as tenancyQuerySchema, T as generateItemsResponseSchema, V as updateExperimentResultBodySchema, a as batchInsertItemsResponseSchema, b as experimentResultIdPathParams, c as compareExperimentsBodySchema, d as datasetAndExperimentIdPathParams, f as datasetAndItemIdPathParams, g as datasetResponseSchema, h as datasetItemVersionPathParams, i as batchInsertItemsBodySchema, j as listExperimentsResponseSchema, k as listDatasetsResponseSchema, l as comparisonResponseSchema, m as datasetItemResponseSchema, n as batchDeleteItemsBodySchema, o as clusterFailuresBodySchema, p as datasetIdPathParams, r as batchDeleteItemsResponseSchema, s as clusterFailuresResponseSchema, t as addItemBodySchema, u as createDatasetBodySchema, w as generateItemsBodySchema, x as experimentResultResponseSchema, y as experimentResponseSchema, z as triggerExperimentBodySchema } from "../../datasets-VSWcmxmV.js";
+import { A as listExperimentResultsResponseSchema, B as updateDatasetBodySchema, C as experimentSummaryResponseSchema, F as paginationQuerySchema, H as updateItemBodySchema, I as reviewSummaryResponseSchema, M as listItemVersionsResponseSchema, N as listItemsQuerySchema, O as listDatasetVersionsResponseSchema, P as listItemsResponseSchema, R as tenancyQuerySchema, T as generateItemsResponseSchema, V as updateExperimentResultBodySchema, a as batchInsertItemsResponseSchema, b as experimentResultIdPathParams, c as compareExperimentsBodySchema, d as datasetAndExperimentIdPathParams, f as datasetAndItemIdPathParams, g as datasetResponseSchema, h as datasetItemVersionPathParams, i as batchInsertItemsBodySchema, j as listExperimentsResponseSchema, k as listDatasetsResponseSchema, l as comparisonResponseSchema, m as datasetItemResponseSchema, n as batchDeleteItemsBodySchema, o as clusterFailuresBodySchema, p as datasetIdPathParams, r as batchDeleteItemsResponseSchema, s as clusterFailuresResponseSchema, t as addItemBodySchema, u as createDatasetBodySchema, w as generateItemsBodySchema, x as experimentResultResponseSchema, y as experimentResponseSchema, z as triggerExperimentBodySchema } from "../../datasets-D3kXQaMn.js";
 import { z as z$1 } from "zod";
@@ -266,3 +266,3 @@ import { Agent } from "@mastra/core/agent";
 		try {
-			const { externalId, input, groundTruth, requestContext, metadata, source, expectedTrajectory, toolMocks } = params;
+			const { externalId, input, groundTruth, requestContext, metadata, source, expectedTrajectory, toolMocks, unmockedToolPolicy } = params;
 			return await (await mastra.datasets.get({ id: datasetId })).addItem({
@@ -276,3 +276,4 @@ externalId: externalId ?? void 0,
 				expectedTrajectory,
-				toolMocks
+				toolMocks,
+				unmockedToolPolicy
 			});
@@ -338,3 +339,3 @@ } catch (error) {
 		try {
-			const { input, groundTruth, requestContext, metadata, expectedTrajectory, toolMocks } = params;
+			const { input, groundTruth, requestContext, metadata, expectedTrajectory, toolMocks, unmockedToolPolicy } = params;
 			const ds = await mastra.datasets.get({ id: datasetId });
@@ -350,3 +351,4 @@ const existing = await ds.getItem({ itemId });
 				expectedTrajectory,
-				toolMocks
+				toolMocks,
+				unmockedToolPolicy
 			});
@@ -353,0 +355,0 @@ } catch (error) {

dist/server/schemas/datasets.d.ts

@@ -196,2 +196,6 @@ import { z } from 'zod/v4';
     }, z.core.$strip>>>;
+    unmockedToolPolicy: z.ZodOptional<z.ZodEnum<{
+        allow: "allow";
+        deny: "deny";
+    }>>;
     requestContext: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
@@ -345,2 +349,6 @@ metadata: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
     }, z.core.$strip>>>;
+    unmockedToolPolicy: z.ZodOptional<z.ZodEnum<{
+        allow: "allow";
+        deny: "deny";
+    }>>;
     requestContext: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
@@ -424,2 +432,6 @@ metadata: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
     }, z.core.$strip>>>;
+    unmockedToolPolicy: z.ZodOptional<z.ZodEnum<{
+        allow: "allow";
+        deny: "deny";
+    }>>;
     requestContext: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
@@ -521,2 +533,3 @@ metadata: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
                 TOOL_MOCK_EXHAUSTED: "TOOL_MOCK_EXHAUSTED";
+                TOOL_MOCK_NOT_DECLARED: "TOOL_MOCK_NOT_DECLARED";
             }>;
@@ -591,2 +604,3 @@ toolName: z.ZodString;
                     TOOL_MOCK_EXHAUSTED: "TOOL_MOCK_EXHAUSTED";
+                    TOOL_MOCK_NOT_DECLARED: "TOOL_MOCK_NOT_DECLARED";
                 }>;
@@ -648,2 +662,6 @@ toolName: z.ZodString;
         }, z.core.$strip>>>;
+        unmockedToolPolicy: z.ZodOptional<z.ZodEnum<{
+            allow: "allow";
+            deny: "deny";
+        }>>;
         requestContext: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
@@ -754,2 +772,3 @@ metadata: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
                     TOOL_MOCK_EXHAUSTED: "TOOL_MOCK_EXHAUSTED";
+                    TOOL_MOCK_NOT_DECLARED: "TOOL_MOCK_NOT_DECLARED";
                 }>;
@@ -806,2 +825,6 @@ toolName: z.ZodString;
     }, z.core.$strip>>>;
+    unmockedToolPolicy: z.ZodOptional<z.ZodEnum<{
+        allow: "allow";
+        deny: "deny";
+    }>>;
     metadata: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
@@ -830,2 +853,6 @@ validTo: z.ZodNullable<z.ZodNumber>;
         }, z.core.$strip>>>;
+        unmockedToolPolicy: z.ZodOptional<z.ZodEnum<{
+            allow: "allow";
+            deny: "deny";
+        }>>;
         metadata: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
@@ -994,2 +1021,6 @@ validTo: z.ZodNullable<z.ZodNumber>;
         }, z.core.$strip>>>;
+        unmockedToolPolicy: z.ZodOptional<z.ZodEnum<{
+            allow: "allow";
+            deny: "deny";
+        }>>;
         requestContext: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
@@ -1028,2 +1059,6 @@ metadata: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
         }, z.core.$strip>>>;
+        unmockedToolPolicy: z.ZodOptional<z.ZodEnum<{
+            allow: "allow";
+            deny: "deny";
+        }>>;
         requestContext: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;
@@ -1030,0 +1065,0 @@ metadata: z.ZodOptional<z.ZodRecord<z.ZodString, z.ZodUnknown>>;

dist/server/schemas/datasets.d.ts.map

@@ -1,1 +0,1 @@
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+{"version":3,"file":"datasets.d.ts","sourceRoot":"","sources":["../../../src/server/schemas/datasets.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,CAAC,EAAE,MAAM,QAAQ,CAAC;AAkO3B,eAAO,MAAM,mBAAmB;;iBAE9B,CAAC;AAEH,eAAO,MAAM,sBAAsB;;iBAEjC,CAAC;AAEH,eAAO,MAAM,gBAAgB;;iBAE3B,CAAC;AAEH,eAAO,MAAM,gCAAgC;;;iBAG3C,CAAC;AAEH,eAAO,MAAM,4BAA4B;;;;iBAIvC,CAAC;AAEH,eAAO,MAAM,0BAA0B;;;iBAGrC,CAAC;AAMH,eAAO,MAAM,qBAAqB;;;iBAGhC,CAAC;AAEH,eAAO,MAAM,kBAAkB;;;iBAG7B,CAAC;AAEH,eAAO,MAAM,oBAAoB;;;;;iBAK/B,CAAC;AAMH,eAAO,MAAM,uBAAuB;;;;;;;;;;iBAUlC,CAAC;AAEH,eAAO,MAAM,uBAAuB;;;;;;;;;;;iBAWlC,CAAC;AAEH,eAAO,MAAM,iBAAiB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAU5B,CAAC;AAEH,eAAO,MAAM,oBAAoB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAS/B,CAAC;AAEH,eAAO,MAAM,2BAA2B;;;;;;;;;;;;;;;;;;;;;;;;;;iBAoBtC,CAAC;AAEH,eAAO,MAAM,4BAA4B;;;iBAGvC,CAAC;AAOH,eAAO,MAAM,qBAAqB;;;;;;;;;;;;;;;iBAehC,CAAC;AAGH,eAAO,MAAM,yBAAyB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAepC,CAAC;AAGH,eAAO,MAAM,wBAAwB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAmBnC,CAAC;AAGH,eAAO,MAAM,kBAAkB;;;;;;iBAM7B,CAAC;AAGH,eAAO,MAAM,8BAA8B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAwBzC,CAAC;AAEH,eAAO,MAAM,gCAAgC;;;;;;;iBAG3C,CAAC;AAmBH,eAAO,MAAM,wBAAwB;;;;;;;;;;;iBAGnC,CAAC;AAIH,eAAO,MAAM,+BAA+B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBA+B1C,CAAC;AAMH,eAAO,MAAM,0BAA0B;;;;;;;;;;;;;;;;;;;;;;;iBAGrC,CAAC;AAEH,eAAO,MAAM,uBAAuB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAGlC,CAAC;AAEH,eAAO,MAAM,6BAA6B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAGxC,CAAC;AAEH,eAAO,MAAM,mCAAmC;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAG9C,CAAC;AAEH,eAAO,MAAM,4BAA4B;;;;;;iBAMvC,CAAC;AAEH,eAAO,MAAM,2BAA2B;;;;;;;;iBAEtC,CAAC;AAOH,eAAO,MAAM,4BAA4B;;;;iBAIvC,CAAC;AAGH,eAAO,MAAM,yBAAyB;;;;;;;;;;;;;;;;;;;;;;;;;iBAcpC,CAAC;AAEH,eAAO,MAAM,8BAA8B;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAEzC,CAAC;AAGH,eAAO,MAAM,4BAA4B;;;;;iBAKvC,CAAC;AAEH,eAAO,MAAM,iCAAiC;;;;;;;;;;;;;iBAG5C,CAAC;AAMH,eAAO,MAAM,0BAA0B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAcrC,CAAC;AAEH,eAAO,MAAM,8BAA8B;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;iBAGzC,CAAC;AAEH,eAAO,MAAM,0BAA0B;;iBAErC,CAAC;AAEH,eAAO,MAAM,8BAA8B;;;iBAGzC,CAAC;AAMH,eAAO,MAAM,uBAAuB;;;;;;;;;iBAYlC,CAAC;AAOH,eAAO,MAAM,2BAA2B;;;;;iBAEtC,CAAC;AAMH,eAAO,MAAM,yBAAyB;;;;;;;;;;;;iBAwBpC,CAAC;AASH,eAAO,MAAM,6BAA6B;;;;;;;;;;;;iBAYxC,CAAC"}

dist/server/schemas/index.cjs

@@ -24,3 +24,3 @@ Object.defineProperty(exports, Symbol.toStringTag, { value: "Module" });
 const require_agent_versions = require("../../agent-versions-DhkWMz0z.cjs");
-const require_datasets = require("../../datasets-BnzmEeJy.cjs");
+const require_datasets = require("../../datasets-BGbh4uCo.cjs");
 const require_editor_builder = require("../../editor-builder-DZRQf2UD.cjs");
@@ -27,0 +27,0 @@ const require_mcp_client_versions = require("../../mcp-client-versions-Db71zP8j.cjs");

dist/server/schemas/index.js

@@ -23,3 +23,3 @@ import { a as messageResponseSchema, c as partialQuerySchema, d as successResponseSchema, f as tracingOptionsSchema, i as createPagePaginationSchema, l as runIdSchema, n as coreMessageSchema, o as optionalRunIdSchema, p as typedPermissive, r as createCombinedPaginationSchema, s as paginationInfoSchema, t as baseLogMessageSchema, u as statusQuerySchema } from "../../common-CPNOpqxA.js";
 import { a as getVersionResponseSchema, c as versionIdPathParams, i as createVersionResponseSchema, n as agentVersionSchema, o as listVersionsResponseSchema, r as compareVersionsResponseSchema, s as restoreVersionResponseSchema, t as agentVersionPathParams } from "../../agent-versions-DC1Rp5EP.js";
-import { A as listExperimentResultsResponseSchema, B as updateDatasetBodySchema, C as experimentSummaryResponseSchema, D as itemVersionResponseSchema, E as itemIdPathParams, F as paginationQuerySchema, H as updateItemBodySchema, I as reviewSummaryResponseSchema, L as scorerResultSchema, M as listItemVersionsResponseSchema, N as listItemsQuerySchema, O as listDatasetVersionsResponseSchema, P as listItemsResponseSchema, R as tenancyQuerySchema, S as experimentReviewCountsSchema, T as generateItemsResponseSchema, V as updateExperimentResultBodySchema, _ as datasetVersionResponseSchema, a as batchInsertItemsResponseSchema, b as experimentResultIdPathParams, c as compareExperimentsBodySchema, d as datasetAndExperimentIdPathParams, f as datasetAndItemIdPathParams, g as datasetResponseSchema, h as datasetItemVersionPathParams, i as batchInsertItemsBodySchema, j as listExperimentsResponseSchema, k as listDatasetsResponseSchema, l as comparisonResponseSchema, m as datasetItemResponseSchema, n as batchDeleteItemsBodySchema, o as clusterFailuresBodySchema, p as datasetIdPathParams, r as batchDeleteItemsResponseSchema, s as clusterFailuresResponseSchema, t as addItemBodySchema, u as createDatasetBodySchema, v as experimentIdPathParams, w as generateItemsBodySchema, x as experimentResultResponseSchema, y as experimentResponseSchema, z as triggerExperimentBodySchema } from "../../datasets-VSWcmxmV.js";
+import { A as listExperimentResultsResponseSchema, B as updateDatasetBodySchema, C as experimentSummaryResponseSchema, D as itemVersionResponseSchema, E as itemIdPathParams, F as paginationQuerySchema, H as updateItemBodySchema, I as reviewSummaryResponseSchema, L as scorerResultSchema, M as listItemVersionsResponseSchema, N as listItemsQuerySchema, O as listDatasetVersionsResponseSchema, P as listItemsResponseSchema, R as tenancyQuerySchema, S as experimentReviewCountsSchema, T as generateItemsResponseSchema, V as updateExperimentResultBodySchema, _ as datasetVersionResponseSchema, a as batchInsertItemsResponseSchema, b as experimentResultIdPathParams, c as compareExperimentsBodySchema, d as datasetAndExperimentIdPathParams, f as datasetAndItemIdPathParams, g as datasetResponseSchema, h as datasetItemVersionPathParams, i as batchInsertItemsBodySchema, j as listExperimentsResponseSchema, k as listDatasetsResponseSchema, l as comparisonResponseSchema, m as datasetItemResponseSchema, n as batchDeleteItemsBodySchema, o as clusterFailuresBodySchema, p as datasetIdPathParams, r as batchDeleteItemsResponseSchema, s as clusterFailuresResponseSchema, t as addItemBodySchema, u as createDatasetBodySchema, v as experimentIdPathParams, w as generateItemsBodySchema, x as experimentResultResponseSchema, y as experimentResponseSchema, z as triggerExperimentBodySchema } from "../../datasets-D3kXQaMn.js";
 import { a as builderModelPolicySchema, c as defaultModelEntrySchema, d as providerModelEntrySchema, i as builderAvailableModelsResponseSchema, l as infrastructureStatusResponseSchema, n as agentFeaturesSchema, o as builderPickerSchema, r as agentModelsSchema, s as builderSettingsResponseSchema, t as agentConfigurationSchema, u as pickerAllowlistSchema } from "../../editor-builder-XFcH8gX0.js";
@@ -26,0 +26,0 @@ import { a as createMCPClientVersionResponseSchema, c as listMCPClientVersionsQuerySchema, d as mcpClientVersionPathParams, f as mcpClientVersionSchema, i as createMCPClientVersionBodySchema, l as listMCPClientVersionsResponseSchema, n as compareMCPClientVersionsQuerySchema, o as deleteMCPClientVersionResponseSchema, p as restoreMCPClientVersionResponseSchema, r as compareMCPClientVersionsResponseSchema, s as getMCPClientVersionResponseSchema, t as activateMCPClientVersionResponseSchema, u as mcpClientVersionIdPathParams } from "../../mcp-client-versions-Dwbiav9P.js";

dist/server/server-adapter/routes/datasets.d.ts.map

@@ -1,1 +0,1 @@
-{"version":3,"file":"datasets.d.ts","sourceRoot":"","sources":["../../../../src/server/server-adapter/routes/datasets.ts"],"names":[],"mappings":"AA4BA,eAAO,MAAM,eAAe;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;yEAqClB,CAAC"}
+{"version":3,"file":"datasets.d.ts","sourceRoot":"","sources":["../../../../src/server/server-adapter/routes/datasets.ts"],"names":[],"mappings":"AA4BA,eAAO,MAAM,eAAe;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;yEAqClB,CAAC"}

package.json

 {
   "name": "@mastra/server",
-  "version": "1.55.0",
+  "version": "1.56.0-alpha.0",
   "description": "",
@@ -119,11 +119,11 @@ "type": "module",
     "zod-to-ts": "^2.1.0",
+    "@internal/lint": "0.0.119",
     "@internal/core": "0.1.0",
-    "@internal/lint": "0.0.119",
     "@internal/storage-test-utils": "0.0.115",
+    "@internal/test-utils": "0.0.55",
     "@internal/types-builder": "0.0.94",
+    "@mastra/agent-builder": "1.1.8",
     "@internal/voice": "0.0.19",
-    "@mastra/agent-builder": "1.1.8",
-    "@mastra/core": "1.55.0",
-    "@internal/test-utils": "0.0.55",
-    "@mastra/schema-compat": "1.3.4"
+    "@mastra/schema-compat": "1.3.4",
+    "@mastra/core": "1.56.0-alpha.0"
   },
@@ -130,0 +130,0 @@ "homepage": "https://mastra.ai",

dist/datasets-BnzmEeJy.cjs

-const require_common = require("./common-spbwhiWS.cjs");
-let zod_v4 = require("zod/v4");
-//#region src/server/schemas/datasets.ts
-const jsonSchemaObject = zod_v4.z.lazy(() => zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()));
-const jsonSchemaField = zod_v4.z.union([jsonSchemaObject, zod_v4.z.null()]).optional();
-const expectedStepBase = {
-	name: zod_v4.z.string().describe("Step name to match"),
-	durationMs: zod_v4.z.number().optional(),
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	children: zod_v4.z.unknown().optional().describe("Nested trajectory expectation (untyped at this depth)")
-};
-const expectedToolCallStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("tool_call"),
-	toolArgs: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	toolResult: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	success: zod_v4.z.boolean().optional()
-});
-const expectedMcpToolCallStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("mcp_tool_call"),
-	toolArgs: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	toolResult: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	mcpServer: zod_v4.z.string().optional(),
-	success: zod_v4.z.boolean().optional()
-});
-const expectedModelGenerationStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("model_generation"),
-	modelId: zod_v4.z.string().optional(),
-	promptTokens: zod_v4.z.number().optional(),
-	completionTokens: zod_v4.z.number().optional(),
-	finishReason: zod_v4.z.string().optional()
-});
-const expectedAgentRunStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("agent_run"),
-	agentId: zod_v4.z.string().optional()
-});
-const expectedWorkflowStepStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("workflow_step"),
-	stepId: zod_v4.z.string().optional(),
-	status: zod_v4.z.string().optional(),
-	output: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional()
-});
-const expectedWorkflowRunStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("workflow_run"),
-	workflowId: zod_v4.z.string().optional(),
-	status: zod_v4.z.string().optional()
-});
-const expectedWorkflowConditionalStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("workflow_conditional"),
-	conditionCount: zod_v4.z.number().optional(),
-	selectedSteps: zod_v4.z.array(zod_v4.z.string()).optional()
-});
-const expectedWorkflowParallelStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("workflow_parallel"),
-	branchCount: zod_v4.z.number().optional(),
-	parallelSteps: zod_v4.z.array(zod_v4.z.string()).optional()
-});
-const expectedWorkflowLoopStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("workflow_loop"),
-	loopType: zod_v4.z.string().optional(),
-	totalIterations: zod_v4.z.number().optional()
-});
-const expectedWorkflowSleepStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("workflow_sleep"),
-	sleepDurationMs: zod_v4.z.number().optional(),
-	sleepType: zod_v4.z.string().optional()
-});
-const expectedWorkflowWaitEventStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("workflow_wait_event"),
-	eventName: zod_v4.z.string().optional(),
-	eventReceived: zod_v4.z.boolean().optional()
-});
-const expectedProcessorRunStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.literal("processor_run"),
-	processorId: zod_v4.z.string().optional()
-});
-const expectedGenericStepSchema = zod_v4.z.object({
-	...expectedStepBase,
-	stepType: zod_v4.z.undefined().optional()
-});
-const expectedStepSchema = zod_v4.z.union([zod_v4.z.discriminatedUnion("stepType", [
-	expectedToolCallStepSchema,
-	expectedMcpToolCallStepSchema,
-	expectedModelGenerationStepSchema,
-	expectedAgentRunStepSchema,
-	expectedWorkflowStepStepSchema,
-	expectedWorkflowRunStepSchema,
-	expectedWorkflowConditionalStepSchema,
-	expectedWorkflowParallelStepSchema,
-	expectedWorkflowLoopStepSchema,
-	expectedWorkflowSleepStepSchema,
-	expectedWorkflowWaitEventStepSchema,
-	expectedProcessorRunStepSchema
-]), expectedGenericStepSchema]);
-const trajectoryExpectationSchema = zod_v4.z.object({
-	steps: zod_v4.z.array(expectedStepSchema).optional().describe("Expected steps for accuracy checking"),
-	ordering: zod_v4.z.enum([
-		"strict",
-		"relaxed",
-		"unordered"
-	]).optional().describe("How to compare step ordering (default: relaxed)"),
-	allowRepeatedSteps: zod_v4.z.boolean().optional().describe("Whether to allow repeated steps (default: true)"),
-	maxSteps: zod_v4.z.number().int().optional().describe("Maximum number of steps allowed"),
-	maxTotalTokens: zod_v4.z.number().int().optional().describe("Maximum total tokens across all model_generation steps"),
-	maxTotalDurationMs: zod_v4.z.number().optional().describe("Maximum total duration in milliseconds"),
-	noRedundantCalls: zod_v4.z.boolean().optional().describe("Whether to penalize redundant calls (same tool + same args consecutively, default: true)"),
-	blacklistedTools: zod_v4.z.array(zod_v4.z.string()).optional().describe("Tool names that should never appear"),
-	blacklistedSequences: zod_v4.z.array(zod_v4.z.array(zod_v4.z.string())).optional().describe("Tool name sequences that should never appear"),
-	maxRetriesPerTool: zod_v4.z.number().int().optional().describe("Maximum retries per tool before penalizing (default: 2)")
-}).optional().nullable().describe("Expected trajectory configuration for trajectory scoring");
-const datasetItemSourceSchema = zod_v4.z.object({
-	type: zod_v4.z.enum([
-		"csv",
-		"json",
-		"trace",
-		"llm",
-		"experiment-result",
-		"candidate-screener"
-	]).describe("How this item was created"),
-	referenceId: zod_v4.z.string().optional().describe("Reference identifier (e.g., trace id, csv filename)")
-}).optional().describe("Source/provenance of this dataset item");
-const itemToolMockSchema = zod_v4.z.object({
-	toolName: zod_v4.z.string().describe("Name of the tool this mock applies to"),
-	args: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).describe("Arguments to match against the tool call"),
-	output: zod_v4.z.unknown().describe("Output served to the agent when matched"),
-	matchArgs: zod_v4.z.enum(["strict", "ignore"]).optional().describe("Argument matching mode. 'strict' (default) deep-equals args; 'ignore' matches on toolName only")
-});
-const toolMocksSchema = zod_v4.z.array(itemToolMockSchema).optional().describe("Ordered item-level static tool mocks served in place of executing the real tool");
-const toolMockReportSchema = zod_v4.z.object({
-	served: zod_v4.z.array(zod_v4.z.object({
-		mockIndex: zod_v4.z.number().int(),
-		toolName: zod_v4.z.string(),
-		args: zod_v4.z.unknown()
-	})),
-	unconsumed: zod_v4.z.array(zod_v4.z.object({
-		mockIndex: zod_v4.z.number().int(),
-		toolName: zod_v4.z.string(),
-		args: zod_v4.z.unknown()
-	})),
-	liveCalls: zod_v4.z.array(zod_v4.z.object({
-		toolName: zod_v4.z.string(),
-		args: zod_v4.z.unknown()
-	})),
-	failure: zod_v4.z.object({
-		code: zod_v4.z.enum(["TOOL_MOCK_MISMATCH", "TOOL_MOCK_EXHAUSTED"]),
-		toolName: zod_v4.z.string(),
-		args: zod_v4.z.unknown()
-	}).optional()
-}).optional().describe("Diagnostic receipt for item-level tool mocks");
-const datasetIdPathParams = zod_v4.z.object({ datasetId: zod_v4.z.string().describe("Unique identifier for the dataset") });
-const experimentIdPathParams = zod_v4.z.object({ experimentId: zod_v4.z.string().describe("Unique identifier for the experiment") });
-const itemIdPathParams = zod_v4.z.object({ itemId: zod_v4.z.string().describe("Unique identifier for the dataset item") });
-const datasetAndExperimentIdPathParams = zod_v4.z.object({
-	datasetId: zod_v4.z.string().describe("Unique identifier for the dataset"),
-	experimentId: zod_v4.z.string().describe("Unique identifier for the experiment")
-});
-const experimentResultIdPathParams = zod_v4.z.object({
-	datasetId: zod_v4.z.string().describe("Unique identifier for the dataset"),
-	experimentId: zod_v4.z.string().describe("Unique identifier for the experiment"),
-	resultId: zod_v4.z.string().describe("Unique identifier for the experiment result")
-});
-const datasetAndItemIdPathParams = zod_v4.z.object({
-	datasetId: zod_v4.z.string().describe("Unique identifier for the dataset"),
-	itemId: zod_v4.z.string().describe("Unique identifier for the dataset item")
-});
-const paginationQuerySchema = zod_v4.z.object({
-	page: zod_v4.z.coerce.number().optional().default(0),
-	perPage: zod_v4.z.coerce.number().optional().default(10)
-});
-const tenancyQuerySchema = zod_v4.z.object({
-	organizationId: zod_v4.z.string().optional().describe("Restrict lookup to the given organization"),
-	projectId: zod_v4.z.string().optional().describe("Restrict lookup to the given project")
-});
-const listItemsQuerySchema = zod_v4.z.object({
-	page: zod_v4.z.coerce.number().optional().default(0),
-	perPage: zod_v4.z.coerce.number().optional().default(10),
-	version: zod_v4.z.coerce.number().int().optional(),
-	search: zod_v4.z.string().optional()
-});
-const createDatasetBodySchema = zod_v4.z.object({
-	name: zod_v4.z.string().describe("Name of the dataset"),
-	description: zod_v4.z.string().optional().describe("Description of the dataset"),
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Additional metadata"),
-	inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
-	groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
-	requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
-	targetType: zod_v4.z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
-	targetIds: zod_v4.z.array(zod_v4.z.string()).optional().describe("IDs of target entities this dataset is attached to"),
-	scorerIds: zod_v4.z.array(zod_v4.z.string()).optional().describe("IDs of scorers attached to this dataset")
-});
-const updateDatasetBodySchema = zod_v4.z.object({
-	name: zod_v4.z.string().optional().describe("Name of the dataset"),
-	description: zod_v4.z.string().optional().describe("Description of the dataset"),
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Additional metadata"),
-	inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
-	groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
-	requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
-	tags: zod_v4.z.array(zod_v4.z.string()).optional().describe("Tag definitions for categorizing experiment results"),
-	targetType: zod_v4.z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
-	targetIds: zod_v4.z.array(zod_v4.z.string()).optional().describe("IDs of target entities this dataset is attached to"),
-	scorerIds: zod_v4.z.array(zod_v4.z.string()).optional().nullable().describe("IDs of scorers attached to this dataset")
-});
-const addItemBodySchema = zod_v4.z.object({
-	externalId: zod_v4.z.string().optional().nullable().describe("Caller-defined, dataset-local item identity"),
-	input: zod_v4.z.unknown().describe("Input data for the dataset item"),
-	groundTruth: zod_v4.z.unknown().optional().describe("Expected output for comparison"),
-	expectedTrajectory: trajectoryExpectationSchema,
-	toolMocks: toolMocksSchema,
-	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Request context preset for this item"),
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Additional metadata"),
-	source: datasetItemSourceSchema
-});
-const updateItemBodySchema = zod_v4.z.object({
-	input: zod_v4.z.unknown().optional().describe("Input data for the dataset item"),
-	groundTruth: zod_v4.z.unknown().optional().describe("Expected output for comparison"),
-	expectedTrajectory: trajectoryExpectationSchema,
-	toolMocks: toolMocksSchema,
-	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Request context preset for this item"),
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Additional metadata"),
-	source: datasetItemSourceSchema
-});
-const triggerExperimentBodySchema = zod_v4.z.object({
-	targetType: zod_v4.z.enum([
-		"agent",
-		"workflow",
-		"scorer"
-	]).describe("Type of target to run against"),
-	targetId: zod_v4.z.string().describe("ID of the target"),
-	scorerIds: zod_v4.z.array(zod_v4.z.string()).optional().describe("IDs of scorers to apply"),
-	version: zod_v4.z.coerce.number().int().optional().describe("Pin to specific dataset version"),
-	agentVersion: zod_v4.z.string().optional().describe("Agent version ID to use for experiment"),
-	maxConcurrency: zod_v4.z.number().optional().describe("Maximum concurrent executions"),
-	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().describe("Global request context passed to the target"),
-	versions: zod_v4.z.object({
-		agents: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.union([zod_v4.z.object({ versionId: zod_v4.z.string() }), zod_v4.z.object({ status: zod_v4.z.enum(["draft", "published"]) })])).optional(),
-		defaultStatus: zod_v4.z.enum(["draft", "published"]).optional()
-	}).optional().describe("Version overrides for sub-agent delegation during experiment execution")
-});
-const compareExperimentsBodySchema = zod_v4.z.object({
-	experimentIdA: zod_v4.z.string().describe("ID of baseline experiment"),
-	experimentIdB: zod_v4.z.string().describe("ID of candidate experiment")
-});
-const datasetResponseSchema = zod_v4.z.object({
-	id: zod_v4.z.string(),
-	name: zod_v4.z.string(),
-	description: zod_v4.z.string().optional().nullable(),
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional().nullable(),
-	inputSchema: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	groundTruthSchema: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	requestContextSchema: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	tags: zod_v4.z.array(zod_v4.z.string()).optional().nullable(),
-	targetType: zod_v4.z.string().optional().nullable(),
-	targetIds: zod_v4.z.array(zod_v4.z.string()).optional().nullable(),
-	scorerIds: zod_v4.z.array(zod_v4.z.string()).optional().nullable(),
-	version: zod_v4.z.number().int(),
-	createdAt: zod_v4.z.coerce.date(),
-	updatedAt: zod_v4.z.coerce.date()
-});
-const datasetItemResponseSchema = zod_v4.z.object({
-	id: zod_v4.z.string(),
-	datasetId: zod_v4.z.string(),
-	datasetVersion: zod_v4.z.number().int(),
-	externalId: zod_v4.z.string().optional().nullable(),
-	input: zod_v4.z.unknown(),
-	groundTruth: zod_v4.z.unknown().optional(),
-	expectedTrajectory: zod_v4.z.unknown().optional(),
-	toolMocks: toolMocksSchema,
-	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	source: datasetItemSourceSchema,
-	createdAt: zod_v4.z.coerce.date(),
-	updatedAt: zod_v4.z.coerce.date()
-});
-const experimentResponseSchema = zod_v4.z.object({
-	id: zod_v4.z.string(),
-	datasetId: zod_v4.z.string().nullable(),
-	datasetVersion: zod_v4.z.number().int().nullable(),
-	agentVersion: zod_v4.z.string().nullable().optional(),
-	targetType: zod_v4.z.enum([
-		"agent",
-		"workflow",
-		"scorer",
-		"processor"
-	]),
-	targetId: zod_v4.z.string(),
-	name: zod_v4.z.string().optional(),
-	description: zod_v4.z.string().optional(),
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	status: zod_v4.z.enum([
-		"pending",
-		"running",
-		"completed",
-		"failed"
-	]),
-	totalItems: zod_v4.z.number(),
-	succeededCount: zod_v4.z.number(),
-	failedCount: zod_v4.z.number(),
-	skippedCount: zod_v4.z.number(),
-	startedAt: zod_v4.z.coerce.date().nullable(),
-	completedAt: zod_v4.z.coerce.date().nullable(),
-	createdAt: zod_v4.z.coerce.date(),
-	updatedAt: zod_v4.z.coerce.date()
-});
-const scorerResultSchema = zod_v4.z.object({
-	scorerId: zod_v4.z.string(),
-	scorerName: zod_v4.z.string(),
-	score: zod_v4.z.number().nullable(),
-	reason: zod_v4.z.string().nullable(),
-	error: zod_v4.z.string().nullable()
-});
-const experimentResultResponseSchema = zod_v4.z.object({
-	id: zod_v4.z.string(),
-	experimentId: zod_v4.z.string(),
-	itemId: zod_v4.z.string(),
-	itemDatasetVersion: zod_v4.z.number().int().nullable(),
-	input: zod_v4.z.unknown(),
-	output: zod_v4.z.unknown().nullable(),
-	groundTruth: zod_v4.z.unknown().nullable(),
-	expectedTrajectory: zod_v4.z.unknown().optional(),
-	error: zod_v4.z.object({
-		message: zod_v4.z.string(),
-		stack: zod_v4.z.string().optional(),
-		code: zod_v4.z.string().optional()
-	}).nullable(),
-	startedAt: zod_v4.z.coerce.date(),
-	completedAt: zod_v4.z.coerce.date(),
-	retryCount: zod_v4.z.number(),
-	traceId: zod_v4.z.string().nullable(),
-	status: zod_v4.z.enum([
-		"needs-review",
-		"reviewed",
-		"complete"
-	]).nullable().optional(),
-	tags: zod_v4.z.array(zod_v4.z.string()).nullable().optional(),
-	toolMockReport: toolMockReportSchema.nullable(),
-	createdAt: zod_v4.z.coerce.date()
-});
-const updateExperimentResultBodySchema = zod_v4.z.object({
-	status: zod_v4.z.enum([
-		"needs-review",
-		"reviewed",
-		"complete"
-	]).nullable().optional(),
-	tags: zod_v4.z.array(zod_v4.z.string()).optional()
-});
-const comparisonItemSchema = zod_v4.z.object({
-	itemId: zod_v4.z.string(),
-	input: zod_v4.z.unknown().nullable(),
-	groundTruth: zod_v4.z.unknown().nullable(),
-	results: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.object({
-		output: zod_v4.z.unknown().nullable(),
-		scores: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.number().nullable())
-	}).nullable())
-});
-const comparisonResponseSchema = zod_v4.z.object({
-	baselineId: zod_v4.z.string(),
-	items: zod_v4.z.array(comparisonItemSchema)
-});
-const experimentSummaryResponseSchema = zod_v4.z.object({
-	experimentId: zod_v4.z.string(),
-	status: zod_v4.z.enum([
-		"pending",
-		"running",
-		"completed",
-		"failed"
-	]),
-	totalItems: zod_v4.z.number(),
-	succeededCount: zod_v4.z.number(),
-	failedCount: zod_v4.z.number(),
-	startedAt: zod_v4.z.coerce.date(),
-	completedAt: zod_v4.z.coerce.date().nullable(),
-	results: zod_v4.z.array(zod_v4.z.object({
-		itemId: zod_v4.z.string(),
-		itemDatasetVersion: zod_v4.z.number().int().nullable(),
-		input: zod_v4.z.unknown(),
-		output: zod_v4.z.unknown().nullable(),
-		groundTruth: zod_v4.z.unknown().nullable(),
-		error: zod_v4.z.string().nullable(),
-		startedAt: zod_v4.z.coerce.date(),
-		completedAt: zod_v4.z.coerce.date(),
-		retryCount: zod_v4.z.number(),
-		toolMockReport: toolMockReportSchema.nullable(),
-		scores: zod_v4.z.array(zod_v4.z.object({
-			scorerId: zod_v4.z.string(),
-			scorerName: zod_v4.z.string(),
-			score: zod_v4.z.number().nullable(),
-			reason: zod_v4.z.string().nullable(),
-			error: zod_v4.z.string().nullable()
-		}))
-	}))
-});
-const listDatasetsResponseSchema = zod_v4.z.object({
-	datasets: zod_v4.z.array(datasetResponseSchema),
-	pagination: require_common.paginationInfoSchema
-});
-const listItemsResponseSchema = zod_v4.z.object({
-	items: zod_v4.z.array(datasetItemResponseSchema),
-	pagination: require_common.paginationInfoSchema
-});
-const listExperimentsResponseSchema = zod_v4.z.object({
-	experiments: zod_v4.z.array(experimentResponseSchema),
-	pagination: require_common.paginationInfoSchema
-});
-const listExperimentResultsResponseSchema = zod_v4.z.object({
-	results: zod_v4.z.array(experimentResultResponseSchema),
-	pagination: require_common.paginationInfoSchema
-});
-const experimentReviewCountsSchema = zod_v4.z.object({
-	experimentId: zod_v4.z.string(),
-	total: zod_v4.z.number().int(),
-	needsReview: zod_v4.z.number().int(),
-	reviewed: zod_v4.z.number().int(),
-	complete: zod_v4.z.number().int()
-});
-const reviewSummaryResponseSchema = zod_v4.z.object({ counts: zod_v4.z.array(experimentReviewCountsSchema) });
-const datasetItemVersionPathParams = zod_v4.z.object({
-	datasetId: zod_v4.z.string().describe("Unique identifier for the dataset"),
-	itemId: zod_v4.z.string().describe("Unique identifier for the dataset item"),
-	datasetVersion: zod_v4.z.coerce.number().int().describe("Dataset version number")
-});
-const itemVersionResponseSchema = zod_v4.z.object({
-	id: zod_v4.z.string(),
-	datasetId: zod_v4.z.string(),
-	datasetVersion: zod_v4.z.number().int(),
-	input: zod_v4.z.unknown(),
-	groundTruth: zod_v4.z.unknown().optional(),
-	expectedTrajectory: zod_v4.z.unknown().optional(),
-	toolMocks: toolMocksSchema,
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	validTo: zod_v4.z.number().int().nullable(),
-	isDeleted: zod_v4.z.boolean(),
-	createdAt: zod_v4.z.coerce.date(),
-	updatedAt: zod_v4.z.coerce.date()
-});
-const listItemVersionsResponseSchema = zod_v4.z.object({ history: zod_v4.z.array(itemVersionResponseSchema) });
-const datasetVersionResponseSchema = zod_v4.z.object({
-	id: zod_v4.z.string(),
-	datasetId: zod_v4.z.string(),
-	version: zod_v4.z.number().int(),
-	createdAt: zod_v4.z.coerce.date()
-});
-const listDatasetVersionsResponseSchema = zod_v4.z.object({
-	versions: zod_v4.z.array(datasetVersionResponseSchema),
-	pagination: require_common.paginationInfoSchema
-});
-const batchInsertItemsBodySchema = zod_v4.z.object({ items: zod_v4.z.array(zod_v4.z.object({
-	externalId: zod_v4.z.string().optional().nullable(),
-	input: zod_v4.z.unknown(),
-	groundTruth: zod_v4.z.unknown().optional(),
-	expectedTrajectory: trajectoryExpectationSchema,
-	toolMocks: toolMocksSchema,
-	requestContext: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	metadata: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.unknown()).optional(),
-	source: datasetItemSourceSchema
-})) });
-const batchInsertItemsResponseSchema = zod_v4.z.object({
-	items: zod_v4.z.array(datasetItemResponseSchema),
-	count: zod_v4.z.number()
-});
-const batchDeleteItemsBodySchema = zod_v4.z.object({ itemIds: zod_v4.z.array(zod_v4.z.string()) });
-const batchDeleteItemsResponseSchema = zod_v4.z.object({
-	success: zod_v4.z.boolean(),
-	deletedCount: zod_v4.z.number()
-});
-const generateItemsBodySchema = zod_v4.z.object({
-	modelId: zod_v4.z.string().describe("Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")"),
-	prompt: zod_v4.z.string().describe("Description of the kind of test data to generate"),
-	count: zod_v4.z.number().int().min(1).max(50).default(5).describe("Number of items to generate"),
-	agentContext: zod_v4.z.object({
-		description: zod_v4.z.string().optional(),
-		instructions: zod_v4.z.string().optional(),
-		tools: zod_v4.z.array(zod_v4.z.string()).optional()
-	}).optional().describe("Context about the agent to generate relevant test data")
-});
-const generatedItemSchema = zod_v4.z.object({
-	input: zod_v4.z.unknown(),
-	groundTruth: zod_v4.z.unknown().optional()
-});
-const generateItemsResponseSchema = zod_v4.z.object({ items: zod_v4.z.array(generatedItemSchema) });
-const clusterFailuresBodySchema = zod_v4.z.object({
-	modelId: zod_v4.z.string().describe("Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")"),
-	items: zod_v4.z.array(zod_v4.z.object({
-		id: zod_v4.z.string(),
-		input: zod_v4.z.unknown(),
-		output: zod_v4.z.unknown().optional(),
-		error: zod_v4.z.string().optional(),
-		scores: zod_v4.z.record(zod_v4.z.string(), zod_v4.z.number()).optional(),
-		existingTags: zod_v4.z.array(zod_v4.z.string()).optional().describe("Tags already applied to this item")
-	})).min(1).max(200).describe("Failure items to cluster"),
-	availableTags: zod_v4.z.array(zod_v4.z.string()).optional().describe("Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable."),
-	prompt: zod_v4.z.string().optional().describe("Optional user instructions to guide the analysis (e.g., \"focus on tool usage failures\")")
-});
-const failureClusterSchema = zod_v4.z.object({
-	id: zod_v4.z.string().describe("A unique cluster identifier"),
-	label: zod_v4.z.string().describe("Short label for this failure pattern"),
-	description: zod_v4.z.string().describe("Description of the common pattern"),
-	itemIds: zod_v4.z.array(zod_v4.z.string()).describe("IDs of items belonging to this cluster")
-});
-const clusterFailuresResponseSchema = zod_v4.z.object({
-	clusters: zod_v4.z.array(failureClusterSchema),
-	/** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */
-	proposedTags: zod_v4.z.array(zod_v4.z.object({
-		itemId: zod_v4.z.string(),
-		tags: zod_v4.z.array(zod_v4.z.string()),
-		reason: zod_v4.z.string().describe("Brief explanation of why these tags were assigned to this item")
-	})).optional()
-});
-//#endregion
-Object.defineProperty(exports, "addItemBodySchema", {
-	enumerable: true,
-	get: function() {
-		return addItemBodySchema;
-	}
-});
-Object.defineProperty(exports, "batchDeleteItemsBodySchema", {
-	enumerable: true,
-	get: function() {
-		return batchDeleteItemsBodySchema;
-	}
-});
-Object.defineProperty(exports, "batchDeleteItemsResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return batchDeleteItemsResponseSchema;
-	}
-});
-Object.defineProperty(exports, "batchInsertItemsBodySchema", {
-	enumerable: true,
-	get: function() {
-		return batchInsertItemsBodySchema;
-	}
-});
-Object.defineProperty(exports, "batchInsertItemsResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return batchInsertItemsResponseSchema;
-	}
-});
-Object.defineProperty(exports, "clusterFailuresBodySchema", {
-	enumerable: true,
-	get: function() {
-		return clusterFailuresBodySchema;
-	}
-});
-Object.defineProperty(exports, "clusterFailuresResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return clusterFailuresResponseSchema;
-	}
-});
-Object.defineProperty(exports, "compareExperimentsBodySchema", {
-	enumerable: true,
-	get: function() {
-		return compareExperimentsBodySchema;
-	}
-});
-Object.defineProperty(exports, "comparisonResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return comparisonResponseSchema;
-	}
-});
-Object.defineProperty(exports, "createDatasetBodySchema", {
-	enumerable: true,
-	get: function() {
-		return createDatasetBodySchema;
-	}
-});
-Object.defineProperty(exports, "datasetAndExperimentIdPathParams", {
-	enumerable: true,
-	get: function() {
-		return datasetAndExperimentIdPathParams;
-	}
-});
-Object.defineProperty(exports, "datasetAndItemIdPathParams", {
-	enumerable: true,
-	get: function() {
-		return datasetAndItemIdPathParams;
-	}
-});
-Object.defineProperty(exports, "datasetIdPathParams", {
-	enumerable: true,
-	get: function() {
-		return datasetIdPathParams;
-	}
-});
-Object.defineProperty(exports, "datasetItemResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return datasetItemResponseSchema;
-	}
-});
-Object.defineProperty(exports, "datasetItemVersionPathParams", {
-	enumerable: true,
-	get: function() {
-		return datasetItemVersionPathParams;
-	}
-});
-Object.defineProperty(exports, "datasetResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return datasetResponseSchema;
-	}
-});
-Object.defineProperty(exports, "datasetVersionResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return datasetVersionResponseSchema;
-	}
-});
-Object.defineProperty(exports, "experimentIdPathParams", {
-	enumerable: true,
-	get: function() {
-		return experimentIdPathParams;
-	}
-});
-Object.defineProperty(exports, "experimentResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return experimentResponseSchema;
-	}
-});
-Object.defineProperty(exports, "experimentResultIdPathParams", {
-	enumerable: true,
-	get: function() {
-		return experimentResultIdPathParams;
-	}
-});
-Object.defineProperty(exports, "experimentResultResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return experimentResultResponseSchema;
-	}
-});
-Object.defineProperty(exports, "experimentReviewCountsSchema", {
-	enumerable: true,
-	get: function() {
-		return experimentReviewCountsSchema;
-	}
-});
-Object.defineProperty(exports, "experimentSummaryResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return experimentSummaryResponseSchema;
-	}
-});
-Object.defineProperty(exports, "generateItemsBodySchema", {
-	enumerable: true,
-	get: function() {
-		return generateItemsBodySchema;
-	}
-});
-Object.defineProperty(exports, "generateItemsResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return generateItemsResponseSchema;
-	}
-});
-Object.defineProperty(exports, "itemIdPathParams", {
-	enumerable: true,
-	get: function() {
-		return itemIdPathParams;
-	}
-});
-Object.defineProperty(exports, "itemVersionResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return itemVersionResponseSchema;
-	}
-});
-Object.defineProperty(exports, "listDatasetVersionsResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return listDatasetVersionsResponseSchema;
-	}
-});
-Object.defineProperty(exports, "listDatasetsResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return listDatasetsResponseSchema;
-	}
-});
-Object.defineProperty(exports, "listExperimentResultsResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return listExperimentResultsResponseSchema;
-	}
-});
-Object.defineProperty(exports, "listExperimentsResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return listExperimentsResponseSchema;
-	}
-});
-Object.defineProperty(exports, "listItemVersionsResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return listItemVersionsResponseSchema;
-	}
-});
-Object.defineProperty(exports, "listItemsQuerySchema", {
-	enumerable: true,
-	get: function() {
-		return listItemsQuerySchema;
-	}
-});
-Object.defineProperty(exports, "listItemsResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return listItemsResponseSchema;
-	}
-});
-Object.defineProperty(exports, "paginationQuerySchema", {
-	enumerable: true,
-	get: function() {
-		return paginationQuerySchema;
-	}
-});
-Object.defineProperty(exports, "reviewSummaryResponseSchema", {
-	enumerable: true,
-	get: function() {
-		return reviewSummaryResponseSchema;
-	}
-});
-Object.defineProperty(exports, "scorerResultSchema", {
-	enumerable: true,
-	get: function() {
-		return scorerResultSchema;
-	}
-});
-Object.defineProperty(exports, "tenancyQuerySchema", {
-	enumerable: true,
-	get: function() {
-		return tenancyQuerySchema;
-	}
-});
-Object.defineProperty(exports, "triggerExperimentBodySchema", {
-	enumerable: true,
-	get: function() {
-		return triggerExperimentBodySchema;
-	}
-});
-Object.defineProperty(exports, "updateDatasetBodySchema", {
-	enumerable: true,
-	get: function() {
-		return updateDatasetBodySchema;
-	}
-});
-Object.defineProperty(exports, "updateExperimentResultBodySchema", {
-	enumerable: true,
-	get: function() {
-		return updateExperimentResultBodySchema;
-	}
-});
-Object.defineProperty(exports, "updateItemBodySchema", {
-	enumerable: true,
-	get: function() {
-		return updateItemBodySchema;
-	}
-});
-
-//# sourceMappingURL=datasets-BnzmEeJy.cjs.map

dist/datasets-BnzmEeJy.cjs.map

-{"version":3,"file":"datasets-BnzmEeJy.cjs","names":["z","paginationInfoSchema"],"sources":["../src/server/schemas/datasets.ts"],"sourcesContent":["import { z } from 'zod/v4';\nimport { paginationInfoSchema } from './common';\n\n// ============================================================================\n// JSON Schema Types (for inputSchema/groundTruthSchema fields)\n// ============================================================================\n\n// JSON Schema type (simplified for storage - full spec too complex)\nconst jsonSchemaObject: z.ZodType<Record<string, unknown>> = z.lazy(() => z.record(z.string(), z.unknown()));\n\n// JSON Schema field (object or null to disable)\nconst jsonSchemaField = z.union([jsonSchemaObject, z.null()]).optional();\n\n// ============================================================================\n// Trajectory Expectation Schema (2 levels deep, children at level 2 use z.unknown())\n// ============================================================================\n\n// Shared base fields for expected steps (level 2 — children typed as z.unknown())\nconst expectedStepBase = {\n  name: z.string().describe('Step name to match'),\n  durationMs: z.number().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  children: z.unknown().optional().describe('Nested trajectory expectation (untyped at this depth)'),\n};\n\n// Typed step variants keyed by stepType\nconst expectedToolCallStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('tool_call'),\n  toolArgs: z.record(z.string(), z.unknown()).optional(),\n  toolResult: z.record(z.string(), z.unknown()).optional(),\n  success: z.boolean().optional(),\n});\n\nconst expectedMcpToolCallStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('mcp_tool_call'),\n  toolArgs: z.record(z.string(), z.unknown()).optional(),\n  toolResult: z.record(z.string(), z.unknown()).optional(),\n  mcpServer: z.string().optional(),\n  success: z.boolean().optional(),\n});\n\nconst expectedModelGenerationStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('model_generation'),\n  modelId: z.string().optional(),\n  promptTokens: z.number().optional(),\n  completionTokens: z.number().optional(),\n  finishReason: z.string().optional(),\n});\n\nconst expectedAgentRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('agent_run'),\n  agentId: z.string().optional(),\n});\n\nconst expectedWorkflowStepStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_step'),\n  stepId: z.string().optional(),\n  status: z.string().optional(),\n  output: z.record(z.string(), z.unknown()).optional(),\n});\n\nconst expectedWorkflowRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_run'),\n  workflowId: z.string().optional(),\n  status: z.string().optional(),\n});\n\nconst expectedWorkflowConditionalStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_conditional'),\n  conditionCount: z.number().optional(),\n  selectedSteps: z.array(z.string()).optional(),\n});\n\nconst expectedWorkflowParallelStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_parallel'),\n  branchCount: z.number().optional(),\n  parallelSteps: z.array(z.string()).optional(),\n});\n\nconst expectedWorkflowLoopStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_loop'),\n  loopType: z.string().optional(),\n  totalIterations: z.number().optional(),\n});\n\nconst expectedWorkflowSleepStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_sleep'),\n  sleepDurationMs: z.number().optional(),\n  sleepType: z.string().optional(),\n});\n\nconst expectedWorkflowWaitEventStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_wait_event'),\n  eventName: z.string().optional(),\n  eventReceived: z.boolean().optional(),\n});\n\nconst expectedProcessorRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('processor_run'),\n  processorId: z.string().optional(),\n});\n\n// Generic step (no stepType — matches by name only)\nconst expectedGenericStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.undefined().optional(),\n});\n\n// Union of all expected step types\nconst expectedStepSchema = z.union([\n  z.discriminatedUnion('stepType', [\n    expectedToolCallStepSchema,\n    expectedMcpToolCallStepSchema,\n    expectedModelGenerationStepSchema,\n    expectedAgentRunStepSchema,\n    expectedWorkflowStepStepSchema,\n    expectedWorkflowRunStepSchema,\n    expectedWorkflowConditionalStepSchema,\n    expectedWorkflowParallelStepSchema,\n    expectedWorkflowLoopStepSchema,\n    expectedWorkflowSleepStepSchema,\n    expectedWorkflowWaitEventStepSchema,\n    expectedProcessorRunStepSchema,\n  ]),\n  expectedGenericStepSchema,\n]);\n\n// Full TrajectoryExpectation schema\nconst trajectoryExpectationSchema = z\n  .object({\n    // Accuracy\n    steps: z.array(expectedStepSchema).optional().describe('Expected steps for accuracy checking'),\n    ordering: z\n      .enum(['strict', 'relaxed', 'unordered'])\n      .optional()\n      .describe('How to compare step ordering (default: relaxed)'),\n    allowRepeatedSteps: z.boolean().optional().describe('Whether to allow repeated steps (default: true)'),\n\n    // Efficiency\n    maxSteps: z.number().int().optional().describe('Maximum number of steps allowed'),\n    maxTotalTokens: z.number().int().optional().describe('Maximum total tokens across all model_generation steps'),\n    maxTotalDurationMs: z.number().optional().describe('Maximum total duration in milliseconds'),\n    noRedundantCalls: z\n      .boolean()\n      .optional()\n      .describe('Whether to penalize redundant calls (same tool + same args consecutively, default: true)'),\n\n    // Blacklist\n    blacklistedTools: z.array(z.string()).optional().describe('Tool names that should never appear'),\n    blacklistedSequences: z\n      .array(z.array(z.string()))\n      .optional()\n      .describe('Tool name sequences that should never appear'),\n\n    // Tool failure tolerance\n    maxRetriesPerTool: z.number().int().optional().describe('Maximum retries per tool before penalizing (default: 2)'),\n  })\n  .optional()\n  .nullable()\n  .describe('Expected trajectory configuration for trajectory scoring');\n\n// Dataset item source tracking\nconst datasetItemSourceSchema = z\n  .object({\n    type: z\n      .enum(['csv', 'json', 'trace', 'llm', 'experiment-result', 'candidate-screener'])\n      .describe('How this item was created'),\n    referenceId: z.string().optional().describe('Reference identifier (e.g., trace id, csv filename)'),\n  })\n  .optional()\n  .describe('Source/provenance of this dataset item');\n\n// Item-level static tool mocks (agent targets only).\nconst itemToolMockSchema = z.object({\n  toolName: z.string().describe('Name of the tool this mock applies to'),\n  args: z.record(z.string(), z.unknown()).describe('Arguments to match against the tool call'),\n  output: z.unknown().describe('Output served to the agent when matched'),\n  matchArgs: z\n    .enum(['strict', 'ignore'])\n    .optional()\n    .describe(\"Argument matching mode. 'strict' (default) deep-equals args; 'ignore' matches on toolName only\"),\n});\n\nconst toolMocksSchema = z\n  .array(itemToolMockSchema)\n  .optional()\n  .describe('Ordered item-level static tool mocks served in place of executing the real tool');\n\n// Diagnostic receipt for item-level tool mocks, persisted on experiment results.\nconst toolMockReportSchema = z\n  .object({\n    served: z.array(z.object({ mockIndex: z.number().int(), toolName: z.string(), args: z.unknown() })),\n    unconsumed: z.array(z.object({ mockIndex: z.number().int(), toolName: z.string(), args: z.unknown() })),\n    liveCalls: z.array(z.object({ toolName: z.string(), args: z.unknown() })),\n    failure: z\n      .object({\n        code: z.enum(['TOOL_MOCK_MISMATCH', 'TOOL_MOCK_EXHAUSTED']),\n        toolName: z.string(),\n        args: z.unknown(),\n      })\n      .optional(),\n  })\n  .optional()\n  .describe('Diagnostic receipt for item-level tool mocks');\n\n// ============================================================================\n// Path Parameter Schemas\n// ============================================================================\n\nexport const datasetIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n});\n\nexport const experimentIdPathParams = z.object({\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n});\n\nexport const itemIdPathParams = z.object({\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n});\n\nexport const datasetAndExperimentIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n});\n\nexport const experimentResultIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n  resultId: z.string().describe('Unique identifier for the experiment result'),\n});\n\nexport const datasetAndItemIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n});\n\n// ============================================================================\n// Query Parameter Schemas\n// ============================================================================\n\nexport const paginationQuerySchema = z.object({\n  page: z.coerce.number().optional().default(0),\n  perPage: z.coerce.number().optional().default(10),\n});\n\nexport const tenancyQuerySchema = z.object({\n  organizationId: z.string().optional().describe('Restrict lookup to the given organization'),\n  projectId: z.string().optional().describe('Restrict lookup to the given project'),\n});\n\nexport const listItemsQuerySchema = z.object({\n  page: z.coerce.number().optional().default(0),\n  perPage: z.coerce.number().optional().default(10),\n  version: z.coerce.number().int().optional(), // Optional version filter for snapshot semantics\n  search: z.string().optional(),\n});\n\n// ============================================================================\n// Request Body Schemas\n// ============================================================================\n\nexport const createDatasetBodySchema = z.object({\n  name: z.string().describe('Name of the dataset'),\n  description: z.string().optional().describe('Description of the dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const updateDatasetBodySchema = z.object({\n  name: z.string().optional().describe('Name of the dataset'),\n  description: z.string().optional().describe('Description of the dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  tags: z.array(z.string()).optional().describe('Tag definitions for categorizing experiment results'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().nullable().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const addItemBodySchema = z.object({\n  externalId: z.string().optional().nullable().describe('Caller-defined, dataset-local item identity'),\n  input: z.unknown().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  toolMocks: toolMocksSchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const updateItemBodySchema = z.object({\n  input: z.unknown().optional().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  toolMocks: toolMocksSchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const triggerExperimentBodySchema = z.object({\n  targetType: z.enum(['agent', 'workflow', 'scorer']).describe('Type of target to run against'),\n  targetId: z.string().describe('ID of the target'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers to apply'),\n  version: z.coerce.number().int().optional().describe('Pin to specific dataset version'),\n  agentVersion: z.string().optional().describe('Agent version ID to use for experiment'),\n  maxConcurrency: z.number().optional().describe('Maximum concurrent executions'),\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Global request context passed to the target'),\n  versions: z\n    .object({\n      agents: z\n        .record(\n          z.string(),\n          z.union([z.object({ versionId: z.string() }), z.object({ status: z.enum(['draft', 'published']) })]),\n        )\n        .optional(),\n      defaultStatus: z.enum(['draft', 'published']).optional(),\n    })\n    .optional()\n    .describe('Version overrides for sub-agent delegation during experiment execution'),\n});\n\nexport const compareExperimentsBodySchema = z.object({\n  experimentIdA: z.string().describe('ID of baseline experiment'),\n  experimentIdB: z.string().describe('ID of candidate experiment'),\n});\n\n// ============================================================================\n// Response Schemas\n// ============================================================================\n\n// Dataset entity schema\nexport const datasetResponseSchema = z.object({\n  id: z.string(),\n  name: z.string(),\n  description: z.string().optional().nullable(),\n  metadata: z.record(z.string(), z.unknown()).optional().nullable(),\n  inputSchema: z.record(z.string(), z.unknown()).optional(),\n  groundTruthSchema: z.record(z.string(), z.unknown()).optional(),\n  requestContextSchema: z.record(z.string(), z.unknown()).optional(),\n  tags: z.array(z.string()).optional().nullable(),\n  targetType: z.string().optional().nullable(),\n  targetIds: z.array(z.string()).optional().nullable(),\n  scorerIds: z.array(z.string()).optional().nullable(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Dataset item entity schema\nexport const datasetItemResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  externalId: z.string().optional().nullable(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  toolMocks: toolMocksSchema,\n  requestContext: z.record(z.string(), z.unknown()).optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  source: datasetItemSourceSchema,\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Experiment entity schema\nexport const experimentResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string().nullable(),\n  datasetVersion: z.number().int().nullable(),\n  agentVersion: z.string().nullable().optional(),\n  targetType: z.enum(['agent', 'workflow', 'scorer', 'processor']),\n  targetId: z.string(),\n  name: z.string().optional(),\n  description: z.string().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  skippedCount: z.number(),\n  startedAt: z.coerce.date().nullable(),\n  completedAt: z.coerce.date().nullable(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Scorer result schema\nexport const scorerResultSchema = z.object({\n  scorerId: z.string(),\n  scorerName: z.string(),\n  score: z.number().nullable(),\n  reason: z.string().nullable(),\n  error: z.string().nullable(),\n});\n\n// Experiment result entity schema\nexport const experimentResultResponseSchema = z.object({\n  id: z.string(),\n  experimentId: z.string(),\n  itemId: z.string(),\n  itemDatasetVersion: z.number().int().nullable(),\n  input: z.unknown(),\n  output: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  expectedTrajectory: z.unknown().optional(),\n  error: z\n    .object({\n      message: z.string(),\n      stack: z.string().optional(),\n      code: z.string().optional(),\n    })\n    .nullable(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date(),\n  retryCount: z.number(),\n  traceId: z.string().nullable(),\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).nullable().optional(),\n  toolMockReport: toolMockReportSchema.nullable(),\n  createdAt: z.coerce.date(),\n});\n\nexport const updateExperimentResultBodySchema = z.object({\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).optional(),\n});\n\n// Comparison item schema (MVP shape)\nconst comparisonItemSchema = z.object({\n  itemId: z.string(),\n  input: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  results: z.record(\n    z.string(),\n    z\n      .object({\n        output: z.unknown().nullable(),\n        scores: z.record(z.string(), z.number().nullable()),\n      })\n      .nullable(),\n  ),\n});\n\n// Comparison result schema\nexport const comparisonResponseSchema = z.object({\n  baselineId: z.string(),\n  items: z.array(comparisonItemSchema),\n});\n\n// Experiment summary schema (returned by trigger experiment)\n// Note: completedAt is nullable for pending/running experiments (async trigger)\nexport const experimentSummaryResponseSchema = z.object({\n  experimentId: z.string(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date().nullable(),\n  results: z.array(\n    z.object({\n      itemId: z.string(),\n      itemDatasetVersion: z.number().int().nullable(),\n      input: z.unknown(),\n      output: z.unknown().nullable(),\n      groundTruth: z.unknown().nullable(),\n      error: z.string().nullable(),\n      startedAt: z.coerce.date(),\n      completedAt: z.coerce.date(),\n      retryCount: z.number(),\n      toolMockReport: toolMockReportSchema.nullable(),\n      scores: z.array(\n        z.object({\n          scorerId: z.string(),\n          scorerName: z.string(),\n          score: z.number().nullable(),\n          reason: z.string().nullable(),\n          error: z.string().nullable(),\n        }),\n      ),\n    }),\n  ),\n});\n\n// ============================================================================\n// List Response Schemas\n// ============================================================================\n\nexport const listDatasetsResponseSchema = z.object({\n  datasets: z.array(datasetResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentsResponseSchema = z.object({\n  experiments: z.array(experimentResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentResultsResponseSchema = z.object({\n  results: z.array(experimentResultResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const experimentReviewCountsSchema = z.object({\n  experimentId: z.string(),\n  total: z.number().int(),\n  needsReview: z.number().int(),\n  reviewed: z.number().int(),\n  complete: z.number().int(),\n});\n\nexport const reviewSummaryResponseSchema = z.object({\n  counts: z.array(experimentReviewCountsSchema),\n});\n\n// ============================================================================\n// Version Schemas\n// ============================================================================\n\n// Path params for item version routes\nexport const datasetItemVersionPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n  datasetVersion: z.coerce.number().int().describe('Dataset version number'),\n});\n\n// Item history row response schema (SCD-2 DatasetItemRow shape)\nexport const itemVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  toolMocks: toolMocksSchema,\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  validTo: z.number().int().nullable(),\n  isDeleted: z.boolean(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\nexport const listItemVersionsResponseSchema = z.object({\n  history: z.array(itemVersionResponseSchema),\n});\n\n// Dataset version response schema\nexport const datasetVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n});\n\nexport const listDatasetVersionsResponseSchema = z.object({\n  versions: z.array(datasetVersionResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\n// ============================================================================\n// Batch Operation Schemas\n// ============================================================================\n\nexport const batchInsertItemsBodySchema = z.object({\n  items: z.array(\n    z.object({\n      externalId: z.string().optional().nullable(),\n      input: z.unknown(),\n      groundTruth: z.unknown().optional(),\n      expectedTrajectory: trajectoryExpectationSchema,\n      toolMocks: toolMocksSchema,\n      requestContext: z.record(z.string(), z.unknown()).optional(),\n      metadata: z.record(z.string(), z.unknown()).optional(),\n      source: datasetItemSourceSchema,\n    }),\n  ),\n});\n\nexport const batchInsertItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  count: z.number(),\n});\n\nexport const batchDeleteItemsBodySchema = z.object({\n  itemIds: z.array(z.string()),\n});\n\nexport const batchDeleteItemsResponseSchema = z.object({\n  success: z.boolean(),\n  deletedCount: z.number(),\n});\n\n// ============================================================================\n// AI Generation Schemas\n// ============================================================================\n\nexport const generateItemsBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  prompt: z.string().describe('Description of the kind of test data to generate'),\n  count: z.number().int().min(1).max(50).default(5).describe('Number of items to generate'),\n  agentContext: z\n    .object({\n      description: z.string().optional(),\n      instructions: z.string().optional(),\n      tools: z.array(z.string()).optional(),\n    })\n    .optional()\n    .describe('Context about the agent to generate relevant test data'),\n});\n\nconst generatedItemSchema = z.object({\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n});\n\nexport const generateItemsResponseSchema = z.object({\n  items: z.array(generatedItemSchema),\n});\n\n// ============================================================================\n// Cluster Failures\n// ============================================================================\n\nexport const clusterFailuresBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  items: z\n    .array(\n      z.object({\n        id: z.string(),\n        input: z.unknown(),\n        output: z.unknown().optional(),\n        error: z.string().optional(),\n        scores: z.record(z.string(), z.number()).optional(),\n        existingTags: z.array(z.string()).optional().describe('Tags already applied to this item'),\n      }),\n    )\n    .min(1)\n    .max(200)\n    .describe('Failure items to cluster'),\n  availableTags: z\n    .array(z.string())\n    .optional()\n    .describe('Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable.'),\n  prompt: z\n    .string()\n    .optional()\n    .describe('Optional user instructions to guide the analysis (e.g., \"focus on tool usage failures\")'),\n});\n\nconst failureClusterSchema = z.object({\n  id: z.string().describe('A unique cluster identifier'),\n  label: z.string().describe('Short label for this failure pattern'),\n  description: z.string().describe('Description of the common pattern'),\n  itemIds: z.array(z.string()).describe('IDs of items belonging to this cluster'),\n});\n\nexport const clusterFailuresResponseSchema = z.object({\n  clusters: z.array(failureClusterSchema),\n  /** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */\n  proposedTags: z\n    .array(\n      z.object({\n        itemId: z.string(),\n        tags: z.array(z.string()),\n        reason: z.string().describe('Brief explanation of why these tags were assigned to this item'),\n      }),\n    )\n    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dist/datasets-VSWcmxmV.js

-import { s as paginationInfoSchema } from "./common-CPNOpqxA.js";
-import { z } from "zod/v4";
-//#region src/server/schemas/datasets.ts
-const jsonSchemaObject = z.lazy(() => z.record(z.string(), z.unknown()));
-const jsonSchemaField = z.union([jsonSchemaObject, z.null()]).optional();
-const expectedStepBase = {
-	name: z.string().describe("Step name to match"),
-	durationMs: z.number().optional(),
-	metadata: z.record(z.string(), z.unknown()).optional(),
-	children: z.unknown().optional().describe("Nested trajectory expectation (untyped at this depth)")
-};
-const expectedToolCallStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("tool_call"),
-	toolArgs: z.record(z.string(), z.unknown()).optional(),
-	toolResult: z.record(z.string(), z.unknown()).optional(),
-	success: z.boolean().optional()
-});
-const expectedMcpToolCallStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("mcp_tool_call"),
-	toolArgs: z.record(z.string(), z.unknown()).optional(),
-	toolResult: z.record(z.string(), z.unknown()).optional(),
-	mcpServer: z.string().optional(),
-	success: z.boolean().optional()
-});
-const expectedModelGenerationStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("model_generation"),
-	modelId: z.string().optional(),
-	promptTokens: z.number().optional(),
-	completionTokens: z.number().optional(),
-	finishReason: z.string().optional()
-});
-const expectedAgentRunStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("agent_run"),
-	agentId: z.string().optional()
-});
-const expectedWorkflowStepStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("workflow_step"),
-	stepId: z.string().optional(),
-	status: z.string().optional(),
-	output: z.record(z.string(), z.unknown()).optional()
-});
-const expectedWorkflowRunStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("workflow_run"),
-	workflowId: z.string().optional(),
-	status: z.string().optional()
-});
-const expectedWorkflowConditionalStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("workflow_conditional"),
-	conditionCount: z.number().optional(),
-	selectedSteps: z.array(z.string()).optional()
-});
-const expectedWorkflowParallelStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("workflow_parallel"),
-	branchCount: z.number().optional(),
-	parallelSteps: z.array(z.string()).optional()
-});
-const expectedWorkflowLoopStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("workflow_loop"),
-	loopType: z.string().optional(),
-	totalIterations: z.number().optional()
-});
-const expectedWorkflowSleepStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("workflow_sleep"),
-	sleepDurationMs: z.number().optional(),
-	sleepType: z.string().optional()
-});
-const expectedWorkflowWaitEventStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("workflow_wait_event"),
-	eventName: z.string().optional(),
-	eventReceived: z.boolean().optional()
-});
-const expectedProcessorRunStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.literal("processor_run"),
-	processorId: z.string().optional()
-});
-const expectedGenericStepSchema = z.object({
-	...expectedStepBase,
-	stepType: z.undefined().optional()
-});
-const expectedStepSchema = z.union([z.discriminatedUnion("stepType", [
-	expectedToolCallStepSchema,
-	expectedMcpToolCallStepSchema,
-	expectedModelGenerationStepSchema,
-	expectedAgentRunStepSchema,
-	expectedWorkflowStepStepSchema,
-	expectedWorkflowRunStepSchema,
-	expectedWorkflowConditionalStepSchema,
-	expectedWorkflowParallelStepSchema,
-	expectedWorkflowLoopStepSchema,
-	expectedWorkflowSleepStepSchema,
-	expectedWorkflowWaitEventStepSchema,
-	expectedProcessorRunStepSchema
-]), expectedGenericStepSchema]);
-const trajectoryExpectationSchema = z.object({
-	steps: z.array(expectedStepSchema).optional().describe("Expected steps for accuracy checking"),
-	ordering: z.enum([
-		"strict",
-		"relaxed",
-		"unordered"
-	]).optional().describe("How to compare step ordering (default: relaxed)"),
-	allowRepeatedSteps: z.boolean().optional().describe("Whether to allow repeated steps (default: true)"),
-	maxSteps: z.number().int().optional().describe("Maximum number of steps allowed"),
-	maxTotalTokens: z.number().int().optional().describe("Maximum total tokens across all model_generation steps"),
-	maxTotalDurationMs: z.number().optional().describe("Maximum total duration in milliseconds"),
-	noRedundantCalls: z.boolean().optional().describe("Whether to penalize redundant calls (same tool + same args consecutively, default: true)"),
-	blacklistedTools: z.array(z.string()).optional().describe("Tool names that should never appear"),
-	blacklistedSequences: z.array(z.array(z.string())).optional().describe("Tool name sequences that should never appear"),
-	maxRetriesPerTool: z.number().int().optional().describe("Maximum retries per tool before penalizing (default: 2)")
-}).optional().nullable().describe("Expected trajectory configuration for trajectory scoring");
-const datasetItemSourceSchema = z.object({
-	type: z.enum([
-		"csv",
-		"json",
-		"trace",
-		"llm",
-		"experiment-result",
-		"candidate-screener"
-	]).describe("How this item was created"),
-	referenceId: z.string().optional().describe("Reference identifier (e.g., trace id, csv filename)")
-}).optional().describe("Source/provenance of this dataset item");
-const itemToolMockSchema = z.object({
-	toolName: z.string().describe("Name of the tool this mock applies to"),
-	args: z.record(z.string(), z.unknown()).describe("Arguments to match against the tool call"),
-	output: z.unknown().describe("Output served to the agent when matched"),
-	matchArgs: z.enum(["strict", "ignore"]).optional().describe("Argument matching mode. 'strict' (default) deep-equals args; 'ignore' matches on toolName only")
-});
-const toolMocksSchema = z.array(itemToolMockSchema).optional().describe("Ordered item-level static tool mocks served in place of executing the real tool");
-const toolMockReportSchema = z.object({
-	served: z.array(z.object({
-		mockIndex: z.number().int(),
-		toolName: z.string(),
-		args: z.unknown()
-	})),
-	unconsumed: z.array(z.object({
-		mockIndex: z.number().int(),
-		toolName: z.string(),
-		args: z.unknown()
-	})),
-	liveCalls: z.array(z.object({
-		toolName: z.string(),
-		args: z.unknown()
-	})),
-	failure: z.object({
-		code: z.enum(["TOOL_MOCK_MISMATCH", "TOOL_MOCK_EXHAUSTED"]),
-		toolName: z.string(),
-		args: z.unknown()
-	}).optional()
-}).optional().describe("Diagnostic receipt for item-level tool mocks");
-const datasetIdPathParams = z.object({ datasetId: z.string().describe("Unique identifier for the dataset") });
-const experimentIdPathParams = z.object({ experimentId: z.string().describe("Unique identifier for the experiment") });
-const itemIdPathParams = z.object({ itemId: z.string().describe("Unique identifier for the dataset item") });
-const datasetAndExperimentIdPathParams = z.object({
-	datasetId: z.string().describe("Unique identifier for the dataset"),
-	experimentId: z.string().describe("Unique identifier for the experiment")
-});
-const experimentResultIdPathParams = z.object({
-	datasetId: z.string().describe("Unique identifier for the dataset"),
-	experimentId: z.string().describe("Unique identifier for the experiment"),
-	resultId: z.string().describe("Unique identifier for the experiment result")
-});
-const datasetAndItemIdPathParams = z.object({
-	datasetId: z.string().describe("Unique identifier for the dataset"),
-	itemId: z.string().describe("Unique identifier for the dataset item")
-});
-const paginationQuerySchema = z.object({
-	page: z.coerce.number().optional().default(0),
-	perPage: z.coerce.number().optional().default(10)
-});
-const tenancyQuerySchema = z.object({
-	organizationId: z.string().optional().describe("Restrict lookup to the given organization"),
-	projectId: z.string().optional().describe("Restrict lookup to the given project")
-});
-const listItemsQuerySchema = z.object({
-	page: z.coerce.number().optional().default(0),
-	perPage: z.coerce.number().optional().default(10),
-	version: z.coerce.number().int().optional(),
-	search: z.string().optional()
-});
-const createDatasetBodySchema = z.object({
-	name: z.string().describe("Name of the dataset"),
-	description: z.string().optional().describe("Description of the dataset"),
-	metadata: z.record(z.string(), z.unknown()).optional().describe("Additional metadata"),
-	inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
-	groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
-	requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
-	targetType: z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
-	targetIds: z.array(z.string()).optional().describe("IDs of target entities this dataset is attached to"),
-	scorerIds: z.array(z.string()).optional().describe("IDs of scorers attached to this dataset")
-});
-const updateDatasetBodySchema = z.object({
-	name: z.string().optional().describe("Name of the dataset"),
-	description: z.string().optional().describe("Description of the dataset"),
-	metadata: z.record(z.string(), z.unknown()).optional().describe("Additional metadata"),
-	inputSchema: jsonSchemaField.describe("JSON Schema for validating item input"),
-	groundTruthSchema: jsonSchemaField.describe("JSON Schema for validating item groundTruth"),
-	requestContextSchema: jsonSchemaField.describe("JSON Schema describing expected request context shape"),
-	tags: z.array(z.string()).optional().describe("Tag definitions for categorizing experiment results"),
-	targetType: z.string().optional().describe("Target entity type (e.g. agent, workflow, scorer)"),
-	targetIds: z.array(z.string()).optional().describe("IDs of target entities this dataset is attached to"),
-	scorerIds: z.array(z.string()).optional().nullable().describe("IDs of scorers attached to this dataset")
-});
-const addItemBodySchema = z.object({
-	externalId: z.string().optional().nullable().describe("Caller-defined, dataset-local item identity"),
-	input: z.unknown().describe("Input data for the dataset item"),
-	groundTruth: z.unknown().optional().describe("Expected output for comparison"),
-	expectedTrajectory: trajectoryExpectationSchema,
-	toolMocks: toolMocksSchema,
-	requestContext: z.record(z.string(), z.unknown()).optional().describe("Request context preset for this item"),
-	metadata: z.record(z.string(), z.unknown()).optional().describe("Additional metadata"),
-	source: datasetItemSourceSchema
-});
-const updateItemBodySchema = z.object({
-	input: z.unknown().optional().describe("Input data for the dataset item"),
-	groundTruth: z.unknown().optional().describe("Expected output for comparison"),
-	expectedTrajectory: trajectoryExpectationSchema,
-	toolMocks: toolMocksSchema,
-	requestContext: z.record(z.string(), z.unknown()).optional().describe("Request context preset for this item"),
-	metadata: z.record(z.string(), z.unknown()).optional().describe("Additional metadata"),
-	source: datasetItemSourceSchema
-});
-const triggerExperimentBodySchema = z.object({
-	targetType: z.enum([
-		"agent",
-		"workflow",
-		"scorer"
-	]).describe("Type of target to run against"),
-	targetId: z.string().describe("ID of the target"),
-	scorerIds: z.array(z.string()).optional().describe("IDs of scorers to apply"),
-	version: z.coerce.number().int().optional().describe("Pin to specific dataset version"),
-	agentVersion: z.string().optional().describe("Agent version ID to use for experiment"),
-	maxConcurrency: z.number().optional().describe("Maximum concurrent executions"),
-	requestContext: z.record(z.string(), z.unknown()).optional().describe("Global request context passed to the target"),
-	versions: z.object({
-		agents: z.record(z.string(), z.union([z.object({ versionId: z.string() }), z.object({ status: z.enum(["draft", "published"]) })])).optional(),
-		defaultStatus: z.enum(["draft", "published"]).optional()
-	}).optional().describe("Version overrides for sub-agent delegation during experiment execution")
-});
-const compareExperimentsBodySchema = z.object({
-	experimentIdA: z.string().describe("ID of baseline experiment"),
-	experimentIdB: z.string().describe("ID of candidate experiment")
-});
-const datasetResponseSchema = z.object({
-	id: z.string(),
-	name: z.string(),
-	description: z.string().optional().nullable(),
-	metadata: z.record(z.string(), z.unknown()).optional().nullable(),
-	inputSchema: z.record(z.string(), z.unknown()).optional(),
-	groundTruthSchema: z.record(z.string(), z.unknown()).optional(),
-	requestContextSchema: z.record(z.string(), z.unknown()).optional(),
-	tags: z.array(z.string()).optional().nullable(),
-	targetType: z.string().optional().nullable(),
-	targetIds: z.array(z.string()).optional().nullable(),
-	scorerIds: z.array(z.string()).optional().nullable(),
-	version: z.number().int(),
-	createdAt: z.coerce.date(),
-	updatedAt: z.coerce.date()
-});
-const datasetItemResponseSchema = z.object({
-	id: z.string(),
-	datasetId: z.string(),
-	datasetVersion: z.number().int(),
-	externalId: z.string().optional().nullable(),
-	input: z.unknown(),
-	groundTruth: z.unknown().optional(),
-	expectedTrajectory: z.unknown().optional(),
-	toolMocks: toolMocksSchema,
-	requestContext: z.record(z.string(), z.unknown()).optional(),
-	metadata: z.record(z.string(), z.unknown()).optional(),
-	source: datasetItemSourceSchema,
-	createdAt: z.coerce.date(),
-	updatedAt: z.coerce.date()
-});
-const experimentResponseSchema = z.object({
-	id: z.string(),
-	datasetId: z.string().nullable(),
-	datasetVersion: z.number().int().nullable(),
-	agentVersion: z.string().nullable().optional(),
-	targetType: z.enum([
-		"agent",
-		"workflow",
-		"scorer",
-		"processor"
-	]),
-	targetId: z.string(),
-	name: z.string().optional(),
-	description: z.string().optional(),
-	metadata: z.record(z.string(), z.unknown()).optional(),
-	status: z.enum([
-		"pending",
-		"running",
-		"completed",
-		"failed"
-	]),
-	totalItems: z.number(),
-	succeededCount: z.number(),
-	failedCount: z.number(),
-	skippedCount: z.number(),
-	startedAt: z.coerce.date().nullable(),
-	completedAt: z.coerce.date().nullable(),
-	createdAt: z.coerce.date(),
-	updatedAt: z.coerce.date()
-});
-const scorerResultSchema = z.object({
-	scorerId: z.string(),
-	scorerName: z.string(),
-	score: z.number().nullable(),
-	reason: z.string().nullable(),
-	error: z.string().nullable()
-});
-const experimentResultResponseSchema = z.object({
-	id: z.string(),
-	experimentId: z.string(),
-	itemId: z.string(),
-	itemDatasetVersion: z.number().int().nullable(),
-	input: z.unknown(),
-	output: z.unknown().nullable(),
-	groundTruth: z.unknown().nullable(),
-	expectedTrajectory: z.unknown().optional(),
-	error: z.object({
-		message: z.string(),
-		stack: z.string().optional(),
-		code: z.string().optional()
-	}).nullable(),
-	startedAt: z.coerce.date(),
-	completedAt: z.coerce.date(),
-	retryCount: z.number(),
-	traceId: z.string().nullable(),
-	status: z.enum([
-		"needs-review",
-		"reviewed",
-		"complete"
-	]).nullable().optional(),
-	tags: z.array(z.string()).nullable().optional(),
-	toolMockReport: toolMockReportSchema.nullable(),
-	createdAt: z.coerce.date()
-});
-const updateExperimentResultBodySchema = z.object({
-	status: z.enum([
-		"needs-review",
-		"reviewed",
-		"complete"
-	]).nullable().optional(),
-	tags: z.array(z.string()).optional()
-});
-const comparisonItemSchema = z.object({
-	itemId: z.string(),
-	input: z.unknown().nullable(),
-	groundTruth: z.unknown().nullable(),
-	results: z.record(z.string(), z.object({
-		output: z.unknown().nullable(),
-		scores: z.record(z.string(), z.number().nullable())
-	}).nullable())
-});
-const comparisonResponseSchema = z.object({
-	baselineId: z.string(),
-	items: z.array(comparisonItemSchema)
-});
-const experimentSummaryResponseSchema = z.object({
-	experimentId: z.string(),
-	status: z.enum([
-		"pending",
-		"running",
-		"completed",
-		"failed"
-	]),
-	totalItems: z.number(),
-	succeededCount: z.number(),
-	failedCount: z.number(),
-	startedAt: z.coerce.date(),
-	completedAt: z.coerce.date().nullable(),
-	results: z.array(z.object({
-		itemId: z.string(),
-		itemDatasetVersion: z.number().int().nullable(),
-		input: z.unknown(),
-		output: z.unknown().nullable(),
-		groundTruth: z.unknown().nullable(),
-		error: z.string().nullable(),
-		startedAt: z.coerce.date(),
-		completedAt: z.coerce.date(),
-		retryCount: z.number(),
-		toolMockReport: toolMockReportSchema.nullable(),
-		scores: z.array(z.object({
-			scorerId: z.string(),
-			scorerName: z.string(),
-			score: z.number().nullable(),
-			reason: z.string().nullable(),
-			error: z.string().nullable()
-		}))
-	}))
-});
-const listDatasetsResponseSchema = z.object({
-	datasets: z.array(datasetResponseSchema),
-	pagination: paginationInfoSchema
-});
-const listItemsResponseSchema = z.object({
-	items: z.array(datasetItemResponseSchema),
-	pagination: paginationInfoSchema
-});
-const listExperimentsResponseSchema = z.object({
-	experiments: z.array(experimentResponseSchema),
-	pagination: paginationInfoSchema
-});
-const listExperimentResultsResponseSchema = z.object({
-	results: z.array(experimentResultResponseSchema),
-	pagination: paginationInfoSchema
-});
-const experimentReviewCountsSchema = z.object({
-	experimentId: z.string(),
-	total: z.number().int(),
-	needsReview: z.number().int(),
-	reviewed: z.number().int(),
-	complete: z.number().int()
-});
-const reviewSummaryResponseSchema = z.object({ counts: z.array(experimentReviewCountsSchema) });
-const datasetItemVersionPathParams = z.object({
-	datasetId: z.string().describe("Unique identifier for the dataset"),
-	itemId: z.string().describe("Unique identifier for the dataset item"),
-	datasetVersion: z.coerce.number().int().describe("Dataset version number")
-});
-const itemVersionResponseSchema = z.object({
-	id: z.string(),
-	datasetId: z.string(),
-	datasetVersion: z.number().int(),
-	input: z.unknown(),
-	groundTruth: z.unknown().optional(),
-	expectedTrajectory: z.unknown().optional(),
-	toolMocks: toolMocksSchema,
-	metadata: z.record(z.string(), z.unknown()).optional(),
-	validTo: z.number().int().nullable(),
-	isDeleted: z.boolean(),
-	createdAt: z.coerce.date(),
-	updatedAt: z.coerce.date()
-});
-const listItemVersionsResponseSchema = z.object({ history: z.array(itemVersionResponseSchema) });
-const datasetVersionResponseSchema = z.object({
-	id: z.string(),
-	datasetId: z.string(),
-	version: z.number().int(),
-	createdAt: z.coerce.date()
-});
-const listDatasetVersionsResponseSchema = z.object({
-	versions: z.array(datasetVersionResponseSchema),
-	pagination: paginationInfoSchema
-});
-const batchInsertItemsBodySchema = z.object({ items: z.array(z.object({
-	externalId: z.string().optional().nullable(),
-	input: z.unknown(),
-	groundTruth: z.unknown().optional(),
-	expectedTrajectory: trajectoryExpectationSchema,
-	toolMocks: toolMocksSchema,
-	requestContext: z.record(z.string(), z.unknown()).optional(),
-	metadata: z.record(z.string(), z.unknown()).optional(),
-	source: datasetItemSourceSchema
-})) });
-const batchInsertItemsResponseSchema = z.object({
-	items: z.array(datasetItemResponseSchema),
-	count: z.number()
-});
-const batchDeleteItemsBodySchema = z.object({ itemIds: z.array(z.string()) });
-const batchDeleteItemsResponseSchema = z.object({
-	success: z.boolean(),
-	deletedCount: z.number()
-});
-const generateItemsBodySchema = z.object({
-	modelId: z.string().describe("Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")"),
-	prompt: z.string().describe("Description of the kind of test data to generate"),
-	count: z.number().int().min(1).max(50).default(5).describe("Number of items to generate"),
-	agentContext: z.object({
-		description: z.string().optional(),
-		instructions: z.string().optional(),
-		tools: z.array(z.string()).optional()
-	}).optional().describe("Context about the agent to generate relevant test data")
-});
-const generatedItemSchema = z.object({
-	input: z.unknown(),
-	groundTruth: z.unknown().optional()
-});
-const generateItemsResponseSchema = z.object({ items: z.array(generatedItemSchema) });
-const clusterFailuresBodySchema = z.object({
-	modelId: z.string().describe("Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")"),
-	items: z.array(z.object({
-		id: z.string(),
-		input: z.unknown(),
-		output: z.unknown().optional(),
-		error: z.string().optional(),
-		scores: z.record(z.string(), z.number()).optional(),
-		existingTags: z.array(z.string()).optional().describe("Tags already applied to this item")
-	})).min(1).max(200).describe("Failure items to cluster"),
-	availableTags: z.array(z.string()).optional().describe("Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable."),
-	prompt: z.string().optional().describe("Optional user instructions to guide the analysis (e.g., \"focus on tool usage failures\")")
-});
-const failureClusterSchema = z.object({
-	id: z.string().describe("A unique cluster identifier"),
-	label: z.string().describe("Short label for this failure pattern"),
-	description: z.string().describe("Description of the common pattern"),
-	itemIds: z.array(z.string()).describe("IDs of items belonging to this cluster")
-});
-const clusterFailuresResponseSchema = z.object({
-	clusters: z.array(failureClusterSchema),
-	/** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */
-	proposedTags: z.array(z.object({
-		itemId: z.string(),
-		tags: z.array(z.string()),
-		reason: z.string().describe("Brief explanation of why these tags were assigned to this item")
-	})).optional()
-});
-//#endregion
-export { listExperimentResultsResponseSchema as A, updateDatasetBodySchema as B, experimentSummaryResponseSchema as C, itemVersionResponseSchema as D, itemIdPathParams as E, paginationQuerySchema as F, updateItemBodySchema as H, reviewSummaryResponseSchema as I, scorerResultSchema as L, listItemVersionsResponseSchema as M, listItemsQuerySchema as N, listDatasetVersionsResponseSchema as O, listItemsResponseSchema as P, tenancyQuerySchema as R, experimentReviewCountsSchema as S, generateItemsResponseSchema as T, updateExperimentResultBodySchema as V, datasetVersionResponseSchema as _, batchInsertItemsResponseSchema as a, experimentResultIdPathParams as b, compareExperimentsBodySchema as c, datasetAndExperimentIdPathParams as d, datasetAndItemIdPathParams as f, datasetResponseSchema as g, datasetItemVersionPathParams as h, batchInsertItemsBodySchema as i, listExperimentsResponseSchema as j, listDatasetsResponseSchema as k, comparisonResponseSchema as l, datasetItemResponseSchema as m, batchDeleteItemsBodySchema as n, clusterFailuresBodySchema as o, datasetIdPathParams as p, batchDeleteItemsResponseSchema as r, clusterFailuresResponseSchema as s, addItemBodySchema as t, createDatasetBodySchema as u, experimentIdPathParams as v, generateItemsBodySchema as w, experimentResultResponseSchema as x, experimentResponseSchema as y, triggerExperimentBodySchema as z };
-
-//# sourceMappingURL=datasets-VSWcmxmV.js.map

dist/datasets-VSWcmxmV.js.map

-{"version":3,"file":"datasets-VSWcmxmV.js","names":[],"sources":["../src/server/schemas/datasets.ts"],"sourcesContent":["import { z } from 'zod/v4';\nimport { paginationInfoSchema } from './common';\n\n// ============================================================================\n// JSON Schema Types (for inputSchema/groundTruthSchema fields)\n// ============================================================================\n\n// JSON Schema type (simplified for storage - full spec too complex)\nconst jsonSchemaObject: z.ZodType<Record<string, unknown>> = z.lazy(() => z.record(z.string(), z.unknown()));\n\n// JSON Schema field (object or null to disable)\nconst jsonSchemaField = z.union([jsonSchemaObject, z.null()]).optional();\n\n// ============================================================================\n// Trajectory Expectation Schema (2 levels deep, children at level 2 use z.unknown())\n// ============================================================================\n\n// Shared base fields for expected steps (level 2 — children typed as z.unknown())\nconst expectedStepBase = {\n  name: z.string().describe('Step name to match'),\n  durationMs: z.number().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  children: z.unknown().optional().describe('Nested trajectory expectation (untyped at this depth)'),\n};\n\n// Typed step variants keyed by stepType\nconst expectedToolCallStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('tool_call'),\n  toolArgs: z.record(z.string(), z.unknown()).optional(),\n  toolResult: z.record(z.string(), z.unknown()).optional(),\n  success: z.boolean().optional(),\n});\n\nconst expectedMcpToolCallStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('mcp_tool_call'),\n  toolArgs: z.record(z.string(), z.unknown()).optional(),\n  toolResult: z.record(z.string(), z.unknown()).optional(),\n  mcpServer: z.string().optional(),\n  success: z.boolean().optional(),\n});\n\nconst expectedModelGenerationStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('model_generation'),\n  modelId: z.string().optional(),\n  promptTokens: z.number().optional(),\n  completionTokens: z.number().optional(),\n  finishReason: z.string().optional(),\n});\n\nconst expectedAgentRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('agent_run'),\n  agentId: z.string().optional(),\n});\n\nconst expectedWorkflowStepStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_step'),\n  stepId: z.string().optional(),\n  status: z.string().optional(),\n  output: z.record(z.string(), z.unknown()).optional(),\n});\n\nconst expectedWorkflowRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_run'),\n  workflowId: z.string().optional(),\n  status: z.string().optional(),\n});\n\nconst expectedWorkflowConditionalStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_conditional'),\n  conditionCount: z.number().optional(),\n  selectedSteps: z.array(z.string()).optional(),\n});\n\nconst expectedWorkflowParallelStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_parallel'),\n  branchCount: z.number().optional(),\n  parallelSteps: z.array(z.string()).optional(),\n});\n\nconst expectedWorkflowLoopStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_loop'),\n  loopType: z.string().optional(),\n  totalIterations: z.number().optional(),\n});\n\nconst expectedWorkflowSleepStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_sleep'),\n  sleepDurationMs: z.number().optional(),\n  sleepType: z.string().optional(),\n});\n\nconst expectedWorkflowWaitEventStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('workflow_wait_event'),\n  eventName: z.string().optional(),\n  eventReceived: z.boolean().optional(),\n});\n\nconst expectedProcessorRunStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.literal('processor_run'),\n  processorId: z.string().optional(),\n});\n\n// Generic step (no stepType — matches by name only)\nconst expectedGenericStepSchema = z.object({\n  ...expectedStepBase,\n  stepType: z.undefined().optional(),\n});\n\n// Union of all expected step types\nconst expectedStepSchema = z.union([\n  z.discriminatedUnion('stepType', [\n    expectedToolCallStepSchema,\n    expectedMcpToolCallStepSchema,\n    expectedModelGenerationStepSchema,\n    expectedAgentRunStepSchema,\n    expectedWorkflowStepStepSchema,\n    expectedWorkflowRunStepSchema,\n    expectedWorkflowConditionalStepSchema,\n    expectedWorkflowParallelStepSchema,\n    expectedWorkflowLoopStepSchema,\n    expectedWorkflowSleepStepSchema,\n    expectedWorkflowWaitEventStepSchema,\n    expectedProcessorRunStepSchema,\n  ]),\n  expectedGenericStepSchema,\n]);\n\n// Full TrajectoryExpectation schema\nconst trajectoryExpectationSchema = z\n  .object({\n    // Accuracy\n    steps: z.array(expectedStepSchema).optional().describe('Expected steps for accuracy checking'),\n    ordering: z\n      .enum(['strict', 'relaxed', 'unordered'])\n      .optional()\n      .describe('How to compare step ordering (default: relaxed)'),\n    allowRepeatedSteps: z.boolean().optional().describe('Whether to allow repeated steps (default: true)'),\n\n    // Efficiency\n    maxSteps: z.number().int().optional().describe('Maximum number of steps allowed'),\n    maxTotalTokens: z.number().int().optional().describe('Maximum total tokens across all model_generation steps'),\n    maxTotalDurationMs: z.number().optional().describe('Maximum total duration in milliseconds'),\n    noRedundantCalls: z\n      .boolean()\n      .optional()\n      .describe('Whether to penalize redundant calls (same tool + same args consecutively, default: true)'),\n\n    // Blacklist\n    blacklistedTools: z.array(z.string()).optional().describe('Tool names that should never appear'),\n    blacklistedSequences: z\n      .array(z.array(z.string()))\n      .optional()\n      .describe('Tool name sequences that should never appear'),\n\n    // Tool failure tolerance\n    maxRetriesPerTool: z.number().int().optional().describe('Maximum retries per tool before penalizing (default: 2)'),\n  })\n  .optional()\n  .nullable()\n  .describe('Expected trajectory configuration for trajectory scoring');\n\n// Dataset item source tracking\nconst datasetItemSourceSchema = z\n  .object({\n    type: z\n      .enum(['csv', 'json', 'trace', 'llm', 'experiment-result', 'candidate-screener'])\n      .describe('How this item was created'),\n    referenceId: z.string().optional().describe('Reference identifier (e.g., trace id, csv filename)'),\n  })\n  .optional()\n  .describe('Source/provenance of this dataset item');\n\n// Item-level static tool mocks (agent targets only).\nconst itemToolMockSchema = z.object({\n  toolName: z.string().describe('Name of the tool this mock applies to'),\n  args: z.record(z.string(), z.unknown()).describe('Arguments to match against the tool call'),\n  output: z.unknown().describe('Output served to the agent when matched'),\n  matchArgs: z\n    .enum(['strict', 'ignore'])\n    .optional()\n    .describe(\"Argument matching mode. 'strict' (default) deep-equals args; 'ignore' matches on toolName only\"),\n});\n\nconst toolMocksSchema = z\n  .array(itemToolMockSchema)\n  .optional()\n  .describe('Ordered item-level static tool mocks served in place of executing the real tool');\n\n// Diagnostic receipt for item-level tool mocks, persisted on experiment results.\nconst toolMockReportSchema = z\n  .object({\n    served: z.array(z.object({ mockIndex: z.number().int(), toolName: z.string(), args: z.unknown() })),\n    unconsumed: z.array(z.object({ mockIndex: z.number().int(), toolName: z.string(), args: z.unknown() })),\n    liveCalls: z.array(z.object({ toolName: z.string(), args: z.unknown() })),\n    failure: z\n      .object({\n        code: z.enum(['TOOL_MOCK_MISMATCH', 'TOOL_MOCK_EXHAUSTED']),\n        toolName: z.string(),\n        args: z.unknown(),\n      })\n      .optional(),\n  })\n  .optional()\n  .describe('Diagnostic receipt for item-level tool mocks');\n\n// ============================================================================\n// Path Parameter Schemas\n// ============================================================================\n\nexport const datasetIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n});\n\nexport const experimentIdPathParams = z.object({\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n});\n\nexport const itemIdPathParams = z.object({\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n});\n\nexport const datasetAndExperimentIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n});\n\nexport const experimentResultIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  experimentId: z.string().describe('Unique identifier for the experiment'),\n  resultId: z.string().describe('Unique identifier for the experiment result'),\n});\n\nexport const datasetAndItemIdPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n});\n\n// ============================================================================\n// Query Parameter Schemas\n// ============================================================================\n\nexport const paginationQuerySchema = z.object({\n  page: z.coerce.number().optional().default(0),\n  perPage: z.coerce.number().optional().default(10),\n});\n\nexport const tenancyQuerySchema = z.object({\n  organizationId: z.string().optional().describe('Restrict lookup to the given organization'),\n  projectId: z.string().optional().describe('Restrict lookup to the given project'),\n});\n\nexport const listItemsQuerySchema = z.object({\n  page: z.coerce.number().optional().default(0),\n  perPage: z.coerce.number().optional().default(10),\n  version: z.coerce.number().int().optional(), // Optional version filter for snapshot semantics\n  search: z.string().optional(),\n});\n\n// ============================================================================\n// Request Body Schemas\n// ============================================================================\n\nexport const createDatasetBodySchema = z.object({\n  name: z.string().describe('Name of the dataset'),\n  description: z.string().optional().describe('Description of the dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const updateDatasetBodySchema = z.object({\n  name: z.string().optional().describe('Name of the dataset'),\n  description: z.string().optional().describe('Description of the dataset'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  inputSchema: jsonSchemaField.describe('JSON Schema for validating item input'),\n  groundTruthSchema: jsonSchemaField.describe('JSON Schema for validating item groundTruth'),\n  requestContextSchema: jsonSchemaField.describe('JSON Schema describing expected request context shape'),\n  tags: z.array(z.string()).optional().describe('Tag definitions for categorizing experiment results'),\n  targetType: z.string().optional().describe('Target entity type (e.g. agent, workflow, scorer)'),\n  targetIds: z.array(z.string()).optional().describe('IDs of target entities this dataset is attached to'),\n  scorerIds: z.array(z.string()).optional().nullable().describe('IDs of scorers attached to this dataset'),\n});\n\nexport const addItemBodySchema = z.object({\n  externalId: z.string().optional().nullable().describe('Caller-defined, dataset-local item identity'),\n  input: z.unknown().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  toolMocks: toolMocksSchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const updateItemBodySchema = z.object({\n  input: z.unknown().optional().describe('Input data for the dataset item'),\n  groundTruth: z.unknown().optional().describe('Expected output for comparison'),\n  expectedTrajectory: trajectoryExpectationSchema,\n  toolMocks: toolMocksSchema,\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Request context preset for this item'),\n  metadata: z.record(z.string(), z.unknown()).optional().describe('Additional metadata'),\n  source: datasetItemSourceSchema,\n});\n\nexport const triggerExperimentBodySchema = z.object({\n  targetType: z.enum(['agent', 'workflow', 'scorer']).describe('Type of target to run against'),\n  targetId: z.string().describe('ID of the target'),\n  scorerIds: z.array(z.string()).optional().describe('IDs of scorers to apply'),\n  version: z.coerce.number().int().optional().describe('Pin to specific dataset version'),\n  agentVersion: z.string().optional().describe('Agent version ID to use for experiment'),\n  maxConcurrency: z.number().optional().describe('Maximum concurrent executions'),\n  requestContext: z.record(z.string(), z.unknown()).optional().describe('Global request context passed to the target'),\n  versions: z\n    .object({\n      agents: z\n        .record(\n          z.string(),\n          z.union([z.object({ versionId: z.string() }), z.object({ status: z.enum(['draft', 'published']) })]),\n        )\n        .optional(),\n      defaultStatus: z.enum(['draft', 'published']).optional(),\n    })\n    .optional()\n    .describe('Version overrides for sub-agent delegation during experiment execution'),\n});\n\nexport const compareExperimentsBodySchema = z.object({\n  experimentIdA: z.string().describe('ID of baseline experiment'),\n  experimentIdB: z.string().describe('ID of candidate experiment'),\n});\n\n// ============================================================================\n// Response Schemas\n// ============================================================================\n\n// Dataset entity schema\nexport const datasetResponseSchema = z.object({\n  id: z.string(),\n  name: z.string(),\n  description: z.string().optional().nullable(),\n  metadata: z.record(z.string(), z.unknown()).optional().nullable(),\n  inputSchema: z.record(z.string(), z.unknown()).optional(),\n  groundTruthSchema: z.record(z.string(), z.unknown()).optional(),\n  requestContextSchema: z.record(z.string(), z.unknown()).optional(),\n  tags: z.array(z.string()).optional().nullable(),\n  targetType: z.string().optional().nullable(),\n  targetIds: z.array(z.string()).optional().nullable(),\n  scorerIds: z.array(z.string()).optional().nullable(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Dataset item entity schema\nexport const datasetItemResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  externalId: z.string().optional().nullable(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  toolMocks: toolMocksSchema,\n  requestContext: z.record(z.string(), z.unknown()).optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  source: datasetItemSourceSchema,\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Experiment entity schema\nexport const experimentResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string().nullable(),\n  datasetVersion: z.number().int().nullable(),\n  agentVersion: z.string().nullable().optional(),\n  targetType: z.enum(['agent', 'workflow', 'scorer', 'processor']),\n  targetId: z.string(),\n  name: z.string().optional(),\n  description: z.string().optional(),\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  skippedCount: z.number(),\n  startedAt: z.coerce.date().nullable(),\n  completedAt: z.coerce.date().nullable(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\n// Scorer result schema\nexport const scorerResultSchema = z.object({\n  scorerId: z.string(),\n  scorerName: z.string(),\n  score: z.number().nullable(),\n  reason: z.string().nullable(),\n  error: z.string().nullable(),\n});\n\n// Experiment result entity schema\nexport const experimentResultResponseSchema = z.object({\n  id: z.string(),\n  experimentId: z.string(),\n  itemId: z.string(),\n  itemDatasetVersion: z.number().int().nullable(),\n  input: z.unknown(),\n  output: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  expectedTrajectory: z.unknown().optional(),\n  error: z\n    .object({\n      message: z.string(),\n      stack: z.string().optional(),\n      code: z.string().optional(),\n    })\n    .nullable(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date(),\n  retryCount: z.number(),\n  traceId: z.string().nullable(),\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).nullable().optional(),\n  toolMockReport: toolMockReportSchema.nullable(),\n  createdAt: z.coerce.date(),\n});\n\nexport const updateExperimentResultBodySchema = z.object({\n  status: z.enum(['needs-review', 'reviewed', 'complete']).nullable().optional(),\n  tags: z.array(z.string()).optional(),\n});\n\n// Comparison item schema (MVP shape)\nconst comparisonItemSchema = z.object({\n  itemId: z.string(),\n  input: z.unknown().nullable(),\n  groundTruth: z.unknown().nullable(),\n  results: z.record(\n    z.string(),\n    z\n      .object({\n        output: z.unknown().nullable(),\n        scores: z.record(z.string(), z.number().nullable()),\n      })\n      .nullable(),\n  ),\n});\n\n// Comparison result schema\nexport const comparisonResponseSchema = z.object({\n  baselineId: z.string(),\n  items: z.array(comparisonItemSchema),\n});\n\n// Experiment summary schema (returned by trigger experiment)\n// Note: completedAt is nullable for pending/running experiments (async trigger)\nexport const experimentSummaryResponseSchema = z.object({\n  experimentId: z.string(),\n  status: z.enum(['pending', 'running', 'completed', 'failed']),\n  totalItems: z.number(),\n  succeededCount: z.number(),\n  failedCount: z.number(),\n  startedAt: z.coerce.date(),\n  completedAt: z.coerce.date().nullable(),\n  results: z.array(\n    z.object({\n      itemId: z.string(),\n      itemDatasetVersion: z.number().int().nullable(),\n      input: z.unknown(),\n      output: z.unknown().nullable(),\n      groundTruth: z.unknown().nullable(),\n      error: z.string().nullable(),\n      startedAt: z.coerce.date(),\n      completedAt: z.coerce.date(),\n      retryCount: z.number(),\n      toolMockReport: toolMockReportSchema.nullable(),\n      scores: z.array(\n        z.object({\n          scorerId: z.string(),\n          scorerName: z.string(),\n          score: z.number().nullable(),\n          reason: z.string().nullable(),\n          error: z.string().nullable(),\n        }),\n      ),\n    }),\n  ),\n});\n\n// ============================================================================\n// List Response Schemas\n// ============================================================================\n\nexport const listDatasetsResponseSchema = z.object({\n  datasets: z.array(datasetResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentsResponseSchema = z.object({\n  experiments: z.array(experimentResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const listExperimentResultsResponseSchema = z.object({\n  results: z.array(experimentResultResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\nexport const experimentReviewCountsSchema = z.object({\n  experimentId: z.string(),\n  total: z.number().int(),\n  needsReview: z.number().int(),\n  reviewed: z.number().int(),\n  complete: z.number().int(),\n});\n\nexport const reviewSummaryResponseSchema = z.object({\n  counts: z.array(experimentReviewCountsSchema),\n});\n\n// ============================================================================\n// Version Schemas\n// ============================================================================\n\n// Path params for item version routes\nexport const datasetItemVersionPathParams = z.object({\n  datasetId: z.string().describe('Unique identifier for the dataset'),\n  itemId: z.string().describe('Unique identifier for the dataset item'),\n  datasetVersion: z.coerce.number().int().describe('Dataset version number'),\n});\n\n// Item history row response schema (SCD-2 DatasetItemRow shape)\nexport const itemVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  datasetVersion: z.number().int(),\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n  expectedTrajectory: z.unknown().optional(),\n  toolMocks: toolMocksSchema,\n  metadata: z.record(z.string(), z.unknown()).optional(),\n  validTo: z.number().int().nullable(),\n  isDeleted: z.boolean(),\n  createdAt: z.coerce.date(),\n  updatedAt: z.coerce.date(),\n});\n\nexport const listItemVersionsResponseSchema = z.object({\n  history: z.array(itemVersionResponseSchema),\n});\n\n// Dataset version response schema\nexport const datasetVersionResponseSchema = z.object({\n  id: z.string(),\n  datasetId: z.string(),\n  version: z.number().int(),\n  createdAt: z.coerce.date(),\n});\n\nexport const listDatasetVersionsResponseSchema = z.object({\n  versions: z.array(datasetVersionResponseSchema),\n  pagination: paginationInfoSchema,\n});\n\n// ============================================================================\n// Batch Operation Schemas\n// ============================================================================\n\nexport const batchInsertItemsBodySchema = z.object({\n  items: z.array(\n    z.object({\n      externalId: z.string().optional().nullable(),\n      input: z.unknown(),\n      groundTruth: z.unknown().optional(),\n      expectedTrajectory: trajectoryExpectationSchema,\n      toolMocks: toolMocksSchema,\n      requestContext: z.record(z.string(), z.unknown()).optional(),\n      metadata: z.record(z.string(), z.unknown()).optional(),\n      source: datasetItemSourceSchema,\n    }),\n  ),\n});\n\nexport const batchInsertItemsResponseSchema = z.object({\n  items: z.array(datasetItemResponseSchema),\n  count: z.number(),\n});\n\nexport const batchDeleteItemsBodySchema = z.object({\n  itemIds: z.array(z.string()),\n});\n\nexport const batchDeleteItemsResponseSchema = z.object({\n  success: z.boolean(),\n  deletedCount: z.number(),\n});\n\n// ============================================================================\n// AI Generation Schemas\n// ============================================================================\n\nexport const generateItemsBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  prompt: z.string().describe('Description of the kind of test data to generate'),\n  count: z.number().int().min(1).max(50).default(5).describe('Number of items to generate'),\n  agentContext: z\n    .object({\n      description: z.string().optional(),\n      instructions: z.string().optional(),\n      tools: z.array(z.string()).optional(),\n    })\n    .optional()\n    .describe('Context about the agent to generate relevant test data'),\n});\n\nconst generatedItemSchema = z.object({\n  input: z.unknown(),\n  groundTruth: z.unknown().optional(),\n});\n\nexport const generateItemsResponseSchema = z.object({\n  items: z.array(generatedItemSchema),\n});\n\n// ============================================================================\n// Cluster Failures\n// ============================================================================\n\nexport const clusterFailuresBodySchema = z.object({\n  modelId: z.string().describe('Model identifier in \"provider/model\" format (e.g., \"openai/gpt-4o\")'),\n  items: z\n    .array(\n      z.object({\n        id: z.string(),\n        input: z.unknown(),\n        output: z.unknown().optional(),\n        error: z.string().optional(),\n        scores: z.record(z.string(), z.number()).optional(),\n        existingTags: z.array(z.string()).optional().describe('Tags already applied to this item'),\n      }),\n    )\n    .min(1)\n    .max(200)\n    .describe('Failure items to cluster'),\n  availableTags: z\n    .array(z.string())\n    .optional()\n    .describe('Existing tag vocabulary from the dataset. The LLM should prefer reusing these tags when applicable.'),\n  prompt: z\n    .string()\n    .optional()\n    .describe('Optional user instructions to guide the analysis (e.g., \"focus on tool usage failures\")'),\n});\n\nconst failureClusterSchema = z.object({\n  id: z.string().describe('A unique cluster identifier'),\n  label: z.string().describe('Short label for this failure pattern'),\n  description: z.string().describe('Description of the common pattern'),\n  itemIds: z.array(z.string()).describe('IDs of items belonging to this cluster'),\n});\n\nexport const clusterFailuresResponseSchema = z.object({\n  clusters: z.array(failureClusterSchema),\n  /** Per-item proposed tag assignments. Each entry maps an item ID to the tags the LLM suggests adding. */\n  proposedTags: z\n    .array(\n      z.object({\n        itemId: z.string(),\n        tags: z.array(z.string()),\n        reason: z.string().describe('Brief explanation of why these tags were assigned to this item'),\n      }),\n    )\n    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New alerts

AI-detected potential code anomaly

Supply chain risk

AI has identified unusual behaviors that may pose a security risk.

No v1

Quality

Package is not semver >=1. This means it is not stable and does not support ^ ranges.

URL strings

Supply chain risk

Package contains fragments of external URLs or IP addresses, which the package may be accessing at runtime.

Fixed alerts

AI-detected potential code anomaly

Supply chain risk

AI has identified unusual behaviors that may pose a security risk.

URL strings

Supply chain risk

Package contains fragments of external URLs or IP addresses, which the package may be accessing at runtime.

Improved metrics

Total package byte prevSize

34697388

0.03%

Lines of code

436775

0.04%

Worsened metrics

Number of low quality alerts

1

Infinity%

No dependency changes