
Research
PyPI Package Impersonates SymPy to Deliver Cryptomining Malware
Malicious PyPI package sympy-dev targets SymPy users, a Python symbolic math library with 85 million monthly downloads.
@thinkhive/mcp-explainer
Advanced tools
MCP server for ThinkHive Explainer API - AI agent trace analysis, RAG evaluation, hallucination detection, and business impact analytics
MCP (Model Context Protocol) server for the ThinkHive Explainer API. Provides AI agents with access to trace analysis, RAG evaluation, hallucination detection, quality metrics, and business impact analytics.
npm install @thinkhive/mcp-explainer
Add to your Claude Code configuration (~/.claude/claude_desktop_config.json):
{
"mcpServers": {
"thinkhive-explainer": {
"command": "npx",
"args": ["@thinkhive/mcp-explainer"],
"env": {
"THINKHIVE_API_KEY": "thk_your_api_key_here",
"THINKHIVE_API_URL": "https://demo.thinkhive.ai"
}
}
}
}
import { startStdioServer } from '@thinkhive/mcp-explainer';
// Start STDIO server
await startStdioServer();
import express from 'express';
import { setupHttpTransport } from '@thinkhive/mcp-explainer';
const app = express();
app.use(express.json());
setupHttpTransport({
app,
basePath: '/mcp',
});
app.listen(3001, () => {
console.log('MCP server running on http://localhost:3001');
});
| Variable | Required | Default | Description |
|---|---|---|---|
THINKHIVE_API_KEY | Yes | - | Your ThinkHive API key |
THINKHIVE_API_URL | No | https://demo.thinkhive.ai | ThinkHive API URL |
| Tool | Description | Tier |
|---|---|---|
trace_explain | Analyze with RAG eval & hallucination detection | Free |
trace_search | Search traces using natural language | Starter+ |
trace_get_explanation | Get stored explanation by trace ID | Free |
trace_patterns | Get failure patterns for an agent | Starter+ |
trace_stats | Get statistics for traces | Free |
trace_batch_analyze | Analyze multiple traces at once | Starter+ |
| Tool | Description | Tier |
|---|---|---|
quality_rag_evaluate | Evaluate RAG quality (groundedness, faithfulness) | Professional+ |
quality_hallucination_detect | Detect hallucinations in responses | Professional+ |
quality_get_scores | Get quality scores for trace/agent | Starter+ |
| Tool | Description | Tier |
|---|---|---|
roi_get_summary | Get ROI summary with revenue impact | Professional+ |
roi_get_correlations | Get performance-to-business correlations | Professional+ |
roi_calculate_impact | Calculate business impact for failures | Professional+ |
| Tool | Description | Tier |
|---|---|---|
cluster_list | List failure clusters for an agent | Starter+ |
cluster_details | Get cluster details with examples | Starter+ |
| Tool | Description | Tier |
|---|---|---|
fix_generate | Generate fix suggestions for failures | Professional+ |
fix_list | List generated fixes | Professional+ |
| Tool | Description | Tier |
|---|---|---|
silent_failures_detect | Detect silent failures | Professional+ |
silent_failures_summary | Get silent failure summary | Professional+ |
| Tool | Description | Tier |
|---|---|---|
webhook_list | List configured webhooks | Professional+ |
webhook_create | Create a new webhook | Professional+ |
webhook_delete | Delete a webhook | Professional+ |
webhook_test | Test a webhook | Professional+ |
| Tool | Description | Tier |
|---|---|---|
eval_run | Run evaluation against golden dataset | Starter+ |
eval_list_sets | List evaluation sets | Starter+ |
eval_get_results | Get evaluation results | Starter+ |
{
"trace": {
"id": "trace-123",
"userMessage": "What's the refund policy?",
"agentResponse": "Our refund policy allows returns within 30 days...",
"outcome": "success"
},
"options": {
"evaluateRAG": true,
"detectHallucinations": true,
"computeBusinessImpact": true
}
}
{
"traceId": "trace-123",
"question": "What's the refund policy?",
"answer": "Our refund policy allows returns within 30 days...",
"contexts": [
"Customers may return items within 30 days of purchase...",
"Refunds are processed within 5-7 business days..."
]
}
{
"agentId": "customer-support",
"dateRange": {
"from": "2024-01-01T00:00:00Z",
"to": "2024-01-31T23:59:59Z"
},
"includeProjections": true
}
{
"agentId": "sales-assistant",
"signals": [
"repeat_contact",
"escalation",
"negative_feedback"
]
}
| Prompt | Description |
|---|---|
analyze-trace | Analyze a trace and provide insights |
debug-failure | Debug a failed interaction |
explain-cluster | Explain a failure cluster |
quality-report | Generate quality report for an agent |
roi-analysis | Generate ROI analysis |
hallucination-review | Review detected hallucinations |
The ThinkHive API backend uses the latest AI models for maximum accuracy:
| Tier | Rate Limit | Features |
|---|---|---|
| Free | 10/min | Basic explainability |
| Starter | 60/min | + Search, batch analysis, patterns, quality metrics |
| Professional | 300/min | + RAG eval, hallucination detection, ROI analytics, webhooks |
| Enterprise | 1,000/min | All features + dedicated support |
Make sure THINKHIVE_API_KEY environment variable is set.
Wait for the rate limit window to reset (1 minute) or upgrade your tier.
Some features require higher tiers. Check the tier column in the tools table.
MIT
FAQs
MCP server for ThinkHive Explainer API v3 - Run-centric architecture with facts vs inferences, deterministic linking, calibrated predictions, and AI agent observability
The npm package @thinkhive/mcp-explainer receives a total of 51 weekly downloads. As such, @thinkhive/mcp-explainer popularity was classified as not popular.
We found that @thinkhive/mcp-explainer demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Research
Malicious PyPI package sympy-dev targets SymPy users, a Python symbolic math library with 85 million monthly downloads.

Product
Create and share saved alert views with custom tabs on the org alerts page, making it easier for teams to return to consistent, named filter sets.

Product
Socket’s Rust and Cargo support is now generally available, providing dependency analysis and supply chain visibility for Rust projects.