io.github.carlisia/mcp-factcheck

平台与服务

by carlisia

基于 semantic search 与 AI 的 MCP 服务器,可按 MCP specification 对内容进行校验与一致性检查。

什么是 io.github.carlisia/mcp-factcheck

基于 semantic search 与 AI 的 MCP 服务器,可按 MCP specification 对内容进行校验与一致性检查。

README

MCP Fact-Check MCP Server

An MCP Server for validating code or content against the official Model Context Protocol (MCP) specification to ensure technical accuracy and prevent the spread of misinformation.

📦 View in MCP Registry - Available in the official MCP Registry

📋 View Project Roadmap - See planned features and development progress

🏗️ Design Documentation - Technical design and implementation details

Overview

The MCP Fact-Check MCP Server helps ensure technical accuracy when coding or writing about MCP by comparing content against official specifications. It uses:

  • Semantic search with OpenAI embeddings to find relevant specification sections
  • AI-powered validation to detect inaccuracies and suggest corrections
  • Compound claim decomposition to validate complex statements with multiple assertions
  • Multiple spec versions support (draft, 2025-06-18, 2025-03-26, 2024-11-05)

Features

MCP Tools Exposed

  1. check_mcp_claim - Comprehensive validation of MCP-related content

    • Validates multi-claim content (documentation, tutorials, bullet points)
    • Automatically decomposes compound claims (e.g., "X and Y") for accurate validation
    • Provides step-by-step validation workflow
    • Identifies missing best practices and modal verb issues
    • Returns corrected content with confidence scores
  2. check_mcp_quick_fact - Quick fact-checking for single MCP claims

    • Validates single sentences or quick questions
    • Returns concise ✓/✗ verdict with explanation
    • Uses aggressive search strategies for accuracy
    • Perfect for "Does MCP support X?" questions
  3. search_spec - Searches MCP specifications using semantic similarity

    • Returns most relevant specification sections
    • Supports all specification versions
  4. list_spec_versions - Lists available MCP specification versions

    • Shows version dates and descriptions
    • Indicates which version is current

MCP Prompts Available

  1. migrate-mcp-content - Guides content migration between MCP specification versions

    • Validates content against source specification first
    • Identifies changes between specification versions
    • Provides step-by-step migration guidance
    • Works with any type of MCP-related content
    • Preserves the original tone, style, and voice when making corrections or suggestions

    Parameters:

    • current_version (required): Source MCP specification version (e.g., "2024-11-05", "2025-06-18")
    • target_version (required): Target MCP specification version to migrate to (e.g., "draft")
    • update_scope (optional): Determines how aggressive the migration should be
      • critical_only: Fix only critical inaccuracies and breaking changes (minimal changes)
      • enhancement_focused: Fix issues and improve clarity, align with best practices
      • comprehensive: Complete review with all improvements and enhanced clarity
      • Default: comprehensive

Installation

The MCP Fact-Check server is available through the Model Context Protocol registry. Install it directly from your MCP client:

For Claude Desktop and other MCP clients:

  • Search for "mcp-factcheck" in your client's server marketplace
  • Click install
  • Provide your OpenAI API key when prompted

That's it! The server will be automatically configured and ready to use.

For developers: If you need to build from source or contribute to the project, see INSTALL.md for development setup instructions.

Observability

Visual Tracing with Arize Phoenix

For a beautiful, AI-focused trace visualization UI, set up Arize Phoenix:

  1. Install and start Phoenix:
bash
# Install Phoenix
pipx install arize-phoenix

# Start Phoenix server
phoenix serve
  1. Update the Host config to send traces to Phoenix:
json
{
  "mcpServers": {
    "mcp-factcheck": {
      "command": "/path/to/bin/mcp-factcheck-server",
      "args": [
        "--data-dir",
        "/path/to/data/embeddings",
        "--telemetry",
        "--otlp-endpoint",
        "http://localhost:6006"
      ],
      "env": {
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}
  1. View traces at: http://localhost:6006

What you'll see in Phoenix:

  • Beautiful AI-focused interface designed for LLM applications
  • Complete validation pipeline timeline with clear visual hierarchy
  • Embedding generation performance and OpenAI API call tracking
  • Vector search visualization with similarity scores
  • Per-chunk validation confidence levels and quality metrics
  • Cost tracking for OpenAI API usage (or whichever llm is being used for embedding the input content/code)
  • Clean, intuitive navigation focused on AI workflows

Phoenix is specifically designed for AI/ML observability and provides a much more user-friendly experience than traditional tracing tools.

Development

Building

bash
# Build all components
go build -o bin/mfc ./cmd/server
go build -o bin/specloader ./utils/cmd

# Run tests
go test ./...

Updating Specifications

The project includes pre-extracted MCP specifications and embeddings for all versions. To check when the draft specification was last updated, see data/SPEC_METADATA.json:

bash
# View draft update information
cat data/SPEC_METADATA.json | jq '.specs.draft'

To update the draft specification:

bash
./bin/specloader spec --version draft
./bin/specloader embed --version draft
./bin/specloader embed --version draft-fine

To add a new specification version:

bash
./bin/specloader spec --version 2025-12-15
./bin/specloader embed --version 2025-12-15
./bin/specloader embed --version 2025-12-15-fine

All specification extraction dates and source commits are automatically tracked in data/SPEC_METADATA.json.

Testing Tools

Test the server using the included test client:

bash
# Build test client
go build -o bin/factcheck-curl ./cmd/factcheck-curl

# Test tools
./bin/factcheck-curl --cmd ./bin/mfc --data-dir ./data/embeddings tools/list
./bin/factcheck-curl --cmd ./bin/mfc --data-dir ./data/embeddings tools/call validate_content '{"content":"MCP is a protocol"}'

# Test prompts
./bin/factcheck-curl --cmd ./bin/mfc --data-dir ./data/embeddings prompts/list

# Get migration prompt with minimal parameters
./bin/factcheck-curl --cmd ./bin/mfc --data-dir ./data/embeddings prompts/get migrate-mcp-content '{"current_version":"2024-11-05","target_version":"draft"}'

# Get migration prompt with all parameters
./bin/factcheck-curl --cmd ./bin/mfc --data-dir ./data/embeddings prompts/get migrate-mcp-content '{
  "current_version": "2024-11-05",
  "target_version": "2025-06-18",
  "update_scope": "critical_only"
}'

Architecture

See DESIGN.md for the complete architecture documentation.

Environment Variables

  • OPENAI_API_KEY - Required for embedding generation and content validation
  • GITHUB_TOKEN - Optional, for higher GitHub API rate limits when extracting specs

License

MIT License. See LICENSE for details.

常见问题

io.github.carlisia/mcp-factcheck 是什么?

基于 semantic search 与 AI 的 MCP 服务器,可按 MCP specification 对内容进行校验与一致性检查。

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