@agentutility/mcp-prooflayer - npm Package Compare versions

Comparing version 0.2.4 to 0.3.1

dist/tools.generated.js

 /** Auto-generated by scripts/generate-mcp-clusters.mjs. Do not edit by hand. */
 export const CLUSTER_SLUG = "prooflayer";
-export const VERSION = "0.2.4";
+export const VERSION = "0.3.1";
 export const TOOLS = [
@@ -23,2 +23,24 @@ {
     {
+        "name": "ai-image-detector",
+        "http_name": "ai-image-detector",
+        "description": "(0.02 USDC/call) AI-generated image detector: send an image URL and get back a calibrated probability that the image is AI-generated or synthetic, plus the vision signals behind the score — anatomical artifacts (hands, teeth, eyes), garbled pseudo-text, over-smooth texture, lighting/shadow inconsistency, background incoherence, and an over-stylized 'AI look'. This is a vision-model heuristic, not a forensic or watermark-level detector, calibrated to never inflate a verdict just because a caller wants one. Verdict buckets: likely_real below 0.4, uncertain 0.4-0.65, likely_ai at 0.65+. A failed or unparseable vision call returns an error instead of a guess. Use it as an AI image detector, synthetic image checker, AI-generated picture screen, or image slop filter.",
+        "method": "POST",
+        "input_schema": {
+            "type": "object",
+            "properties": {
+                "image_url": {
+                    "type": "string",
+                    "description": "Direct HTTP/HTTPS URL to the image to assess."
+                },
+                "hint": {
+                    "type": "string",
+                    "description": "Optional context about the image, e.g. 'profile picture' or 'product photo'. Max 500 chars. Used as a hint only, never overrides what the model sees."
+                }
+            },
+            "required": [
+                "image_url"
+            ]
+        }
+    },
+    {
         "name": "app-store-rejection-explain",
@@ -195,2 +217,24 @@ "http_name": "app-store-rejection-explain",
     {
+        "name": "prompt-injection-detect",
+        "http_name": "prompt-injection-detect",
+        "description": "(0.02 USDC/call) Prompt injection detector for untrusted inbound text — a webpage, an email, a tool result, or a message an agent is about to read. Two layers run on every call: a deterministic pattern layer (pure code) matching known injection shapes — instruction override, system-prompt exfiltration, role hijack / jailbreak phrasing ('act as DAN', 'developer mode'), fake delimiter blocks, tool abuse, and covert manipulation — plus a calibrated LLM judge layer scoring overall injection likelihood 0-1, never inflated to please a caller. Returns which pattern families hit, matched snippets, the judge's reasoning, and a combined verdict. If the LLM leg fails, the deterministic layer alone is still a valid, billable result. Use it as a prompt injection detector, jailbreak detector, untrusted text screen, or LLM input firewall.",
+        "method": "POST",
+        "input_schema": {
+            "type": "object",
+            "properties": {
+                "text": {
+                    "type": "string",
+                    "description": "Untrusted text to screen for prompt-injection attempts, e.g. scraped webpage content, an email body, or a tool result. Max 20000 chars."
+                },
+                "context": {
+                    "type": "string",
+                    "description": "Optional note on what the agent plans to do with this text, e.g. 'summarize for the user'. Max 500 chars, used as a hint only."
+                }
+            },
+            "required": [
+                "text"
+            ]
+        }
+    },
+    {
         "name": "prompt-injection-surface",
@@ -197,0 +241,0 @@ "http_name": "prompt-injection-surface",

package.json

 {
   "name": "@agentutility/mcp-prooflayer",
   "mcpName": "ai.agentutility/mcp-prooflayer",
-  "version": "0.2.4",
+  "version": "0.3.1",
   "description": "MCP server for the @agentutility prooflayer cluster — pay-per-call x402 tools, no API keys, USDC on Base.",
@@ -6,0 +6,0 @@ "license": "MIT",

README.md

@@ -25,3 +25,3 @@ # @agentutility/mcp-prooflayer
 
-Restart Claude Desktop. 15 tools appear in the tool palette.
+Restart Claude Desktop. 17 tools appear in the tool palette.
 
@@ -50,3 +50,3 @@ ## Install — Cursor
 
-## Tools (15)
+## Tools (17)
 
@@ -56,2 +56,3 @@ | Tool | Description |
 | `ai-content-detector` | (0.03 USDC/call) Detect AI-generated writing with a calibrated probability score. Returns a 0-1 likelihood, a verdict, suspicious phrases, and per-axis style signals (em-dash overuse, hedge phrases, formulaic transitions). Works as an AI content detector, GPT detector, or ChatGPT-text checker. |
+| `ai-image-detector` | (0.02 USDC/call) AI-generated image detector: send an image URL and get back a calibrated probability that the image is AI-generated or synthetic, plus the vision signals behind the score — anatomical artifacts (hands, teeth, eyes), garbled pseudo-text, over-smooth texture, lighting/shadow inconsistency, background incoherence, and an over-stylized 'AI look'. This is a vision-model heuristic, not a forensic or watermark-level detector, calibrated to never inflate a verdict just because a caller wants one. Verdict buckets: likely_real below 0.4, uncertain 0.4-0.65, likely_ai at 0.65+. A failed or unparseable vision call returns an error instead of a guess. Use it as an AI image detector, synthetic image checker, AI-generated picture screen, or image slop filter. |
 | `app-store-rejection-explain` | (0.02 USDC/call) Explains App Store and Google Play rejections and turns them into a resubmission plan. Parses rejection text and app metadata, identifies likely policy areas, extracts deadlines, and returns a policy-clean resubmission checklist plus reviewer-note outline. Does not promise approval or recommend platform-policy evasion. Use it for Google Play rejection triage or as a mobile app review policy checklist. |
@@ -67,2 +68,3 @@ | `brand-clearance` | (0.25 USDC/call) Screens a candidate brand name across trademark, domain, and web-presence checks in a single composite signal. Runs four checks in parallel (in-process, no x402 self-billing): USPTO TM name search, domain availability across requested TLDs (default .com .ai .dev .io .co), Wikipedia presence, and a Hacker News mention scan. Returns risk_level (clear|soft|moderate|hard), risk_score 0-100, sub-scores per signal, the raw hits, and a one-line recommendation. Designed for AI agents self-screening project, product, or startup names before committing. Screening tool only, not legal advice. Use it as a brand clearance aggregator, name screening tool, product-name vetting step, or startup-brand pre-flight. |
 | `production-readiness-score` | (0.10 USDC/call) Scores production readiness for a repo in one call, aggregating the Prooflayer cluster. Calls all 5 component scanners (secrets-exposure-check, deploy-config-risk, db-migration-risk, dep-risk-summary, prompt-injection-surface) in parallel in-process and rolls findings into a weighted composite score (default weights: secrets 0.30, migrations 0.20, deps 0.20, deploy 0.15, prompt 0.15). Returns composite 0-100, production_grade (production-ready | needs-review | risky | do-not-ship), per-component sub-scores, top-N deduped findings sorted by severity + score_contribution, and a Venice plain-English verdict. Dual input: {repo: 'owner/name'} or {files: [{path, content}, ...]}. Optional 'weights' override (each in [0,0.5], proportionally normalized) and 'max_findings' (default 10, cap 50). Use it as an AI app deploy gate, repo prod-risk composite, one-call audit, unified production readiness API, or vibe-coded app safety scan. |
+| `prompt-injection-detect` | (0.02 USDC/call) Prompt injection detector for untrusted inbound text — a webpage, an email, a tool result, or a message an agent is about to read. Two layers run on every call: a deterministic pattern layer (pure code) matching known injection shapes — instruction override, system-prompt exfiltration, role hijack / jailbreak phrasing ('act as DAN', 'developer mode'), fake delimiter blocks, tool abuse, and covert manipulation — plus a calibrated LLM judge layer scoring overall injection likelihood 0-1, never inflated to please a caller. Returns which pattern families hit, matched snippets, the judge's reasoning, and a combined verdict. If the LLM leg fails, the deterministic layer alone is still a valid, billable result. Use it as a prompt injection detector, jailbreak detector, untrusted text screen, or LLM input firewall. |
 | `prompt-injection-surface` | (0.03 USDC/call) Scans AI app source code for prompt injection risk at LLM call sites. Walks .ts/.tsx/.js/.jsx/.py/.mjs/.cjs source files, locates LLM SDK call sites (anthropic, openai, @ai-sdk/*, google generative), and flags user input flowing into prompts without sanitization, calls without max_tokens caps, system/user prompt mixing, and LLM output used unvalidated in fetch/exec/eval. Returns 0-100 score, per-finding kind/severity/path/line/evidence/recommendation, and a Venice plain-English verdict. Dual input: {repo: 'owner/name'} (tree-walk, capped 500 files) or {files: [{path, content}, ...]}. Use it as an LLM call-site audit, unsanitized-user-input-in-prompts detector, system-message mixing flag, unbounded completion detector, AI app safety scan, or pre-deploy AI risk gate. |
@@ -92,2 +94,2 @@ | `pypi-package-risk` | (0.01 USDC/call) Scores the supply-chain risk of a PyPI package before you install it. Pulls metadata + release history from pypi.org for a package (and optional version), evaluating age, recent download volume, maintainer count, post-install hook presence, dependency depth, deprecation flags, last-update recency, and string-distance to popular packages (typosquat). Returns a numeric score (0-10), risk_level bucket, contributing factor list, and an AI-written plain-English risk summary from Venice. Use it as a Python supply-chain scanner, pip dependency vetter, typosquat detector, pre-install audit, Python-package safety check, pyproject.toml / requirements.txt vetter, or poetry + uv pre-install gate. |
 
-**Version:** 0.2.4 · **License:** MIT
+**Version:** 0.3.1 · **License:** MIT

New alerts

AI-detected potential code anomaly

Supply chain risk

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

Fixed alerts

AI-detected potential code anomaly

Supply chain risk

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

Improved metrics

Total package byte prevSize

40318

13.78%

Lines of code

438

12.31%

Number of lines in readme file

91

2.25%

No dependency changes