Fact-Checker MCP Server

A Model Context Protocol (MCP) server that provides AI-powered fact-checking and claim verification. Uses Claude Haiku for claim extraction, SPO-based semantic fingerprinting, and Redis caching for deduplication.

Exposes both an MCP (StreamableHTTP) interface for AI agents and a REST API for HTTP clients.

Features

MCP Tools

REST API

Related MCP server: truth-anchor-agent

Quick Start

Prerequisites

• Node.js 20+

• An Anthropic API key

• Redis (optional — caching is gracefully skipped when unavailable)

Install

git clone https://github.com/luminarylane/fact-checker-mcp.git
cd fact-checker-mcp
npm install
npm run build

Configure

Copy the example env file and add your Anthropic API key:

cp .env.example .env
# Edit .env and set ANTHROPIC_API_KEY

Run

# Development (auto-reload)
npm run dev

# Production
npm start

The server starts two listeners:

• REST API on port 8004 (configurable via PORT)

• MCP transport on port 8005 (configurable via MCP_PORT)

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "fact-checker": {
      "command": "node",
      "args": ["/path/to/fact-checker-mcp/dist/index.js"]
    }
  }
}

How It Works

Claim Extraction (extractClaims)

Extracts verifiable claims from text using Claude Haiku. Each claim is classified by type (statistic, company_fact, quote, product_claim, market_assertion, opinion) and durability (permanent, durable, transient, volatile).

Content Verification (verifyContent / GATE)

Full content verification pipeline:

1. Extract claims from content

2. Fingerprint each claim using SPO (Subject-Predicate-Object) triples

3. Check Redis cache for previously verified claims

4. Verify uncached claims via Claude Haiku with optional brand data and web context

5. Return verdicts (publish, revise, block) with corrected content

Signal Verification (verifySignal / SENSE)

Verifies a single inbound claim (e.g., "Company X raised $50M") and returns:

• Signal verdict: verified, likely_true, uncertain, likely_false, contradicted

• Action recommendation: safe_to_act, proceed_with_caution, needs_human_review, do_not_act

Semantic Fingerprinting

Claims are normalized into Subject-Predicate-Object triples via Haiku, then hashed. Equivalent claims (different wording, same meaning) produce the same fingerprint, enabling cache deduplication.

Fact Memory (getFactMemory)

Search previously verified claims by keyword. Useful for avoiding re-verification during multi-step workflows.

Configuration

Architecture

• Transport: StreamableHTTP (MCP) + Express (REST)

• AI: Claude Haiku for claim extraction, verification, and SPO fingerprinting

• Cache: Redis with durability-based TTLs (gracefully degrades without Redis)

• Rate Limiting: Token-bucket rate limiter for Anthropic API calls (100 req/min)

• Safety: Untrusted content wrapped with hashed markers for prompt injection defence

Development

# Development server (auto-reload)
npm run dev

# Type check
npx tsc --noEmit

# Run tests
npm test

# Test with coverage
npm run test:coverage

# Build
npm run build

License

MIT