Mcp Mcp

What is Mcp Mcp about?

Mcp Mcp offers a “phone‑book” of MCP servers, aggregating over a thousand entries from official lists and community collections. It continuously updates the database every few hours, de‑duplicates sources, and stores pre‑computed embeddings for instant semantic search.

How to use Mcp Mcp?

1. Installation – the package can be installed via the Python package manager uvx or pipx:

   uvx mcp-mcp   # runs the latest version without a permanent install
   # or
   pipx install mcp-mcp
   

2. Running the server – choose a transport method:
   • STDIO (default, used by Claude Desktop): uv run main.py
   • HTTP for easier testing: uv run main.py --http --host 0.0.0.0 --port 8000
3. Claude configuration – add an entry to the Claude Desktop or Claude Code config files so the assistant can query the server, e.g.:

   {
     "mcpServers": {
       "mcp-mcp": { "command": "uvx", "args": ["mcp-mcp"] }
     }
   }
   

4. Querying – ask the assistant natural‑language questions such as “Find me an MCP server for weather data” and it will return matching server definitions.

Key Features

• Aggregated database from official MCP servers and two major community lists.
• Intelligent deduplication to guarantee unique entries.
• Automatic updates every 3 hours.
• Semantic search with pre‑computed embeddings for sub‑second responses.
• Multiple transport modes (STDIO for tool integration, HTTP for debugging).
• Full test suite covering unit and integration scenarios, including optional GitHub live‑download tests.
• Convenient development workflow via justfile commands (dev, test, build, publish).

Use Cases

• An AI assistant needs a specific capability (e.g., domain availability check) and uses Mcp Mcp to discover a ready‑to‑run MCP server.
• Developers building custom agents can programmatically query the registry to auto‑populate tool menus.
• Continuous integration pipelines can validate that a required MCP server is present before deployment.
• Researchers can explore the ecosystem of available MCP tools for benchmarking or integration studies.

FAQ

Q: Do I need an API key to use Mcp Mcp? A: No, the server is open‑source and publicly reachable; only optional environment variables are required for private registries, which are not covered in the core MVP.

Q: Which transport should I choose? A: Use STDIO when integrating with Claude Desktop or other MCP‑compatible clients. Use HTTP for local testing or when you need a REST endpoint.

Q: How often is the server list refreshed? A: Every three hours, pulling the latest changes from the curated sources.

Q: Can I contribute new MCP servers? A: Yes – submit a pull request to the source lists (official or community) and the next automated update will include them.

Q: Is Docker support available? A: Docker integration is planned for a future release (see roadmap).

MCP-MCP: Meta-MCP Server

MCP-MCP is a Meta-MCP Server that acts as a tool discovery and provisioning service for the Model Context Protocol (MCP). When an AI assistant needs a capability that isn't currently available, it can ask MCP-MCP to discover and suggest appropriate MCP servers from a comprehensive database of over a thousand servers aggregated from multiple curated sources.

Think of it as a "phone book" for MCP servers - one tool to find all other tools.

🗃️ 1,727+ MCP Servers Available

MCP-MCP provides access to a comprehensive database aggregated from multiple curated sources, including:

• Official MCP Servers (modelcontextprotocol/servers)
• Community Collections (Punkpeye & Appcypher awesome lists)
• Intelligent Deduplication ensures no duplicates across sources

The database is automatically updated every 3 hours with the latest servers from the community.

Motivation

Agents Just Wanna Have Tools

• Agents know what they need: AI assistants can clearly articulate requirements like "check domain availability" or "get weather data"
• Web search isn't always enough: Generic search results don't always provide realtime data
• CLI tools require setup: Many tools need complex installation, configuration, and API keys - agents have to repeat this setup every single time they need to complete a task
• MCP servers are scattered: Great tools exist but discovering them requires manual research across GitHub, forums, and documentation

Why make agents (and users) hunt for tools when we can bring the tools to them?

Quick Start

Claude Desktop Configuration

Add MCP-MCP to your Claude Desktop configuration file:

Configuration File Location:

• macOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Configuration:

{
  "mcpServers": {
    "mcp-mcp": {
      "command": "uvx",
      "args": ["mcp-mcp"]
    }
  }
}

Alternative with pipx:

{
  "mcpServers": {
    "mcp-mcp": {
      "command": "mcp-mcp"
    }
  }
}

Claude Code Configuration

Add MCP-MCP to your Claude Code configuration file:

claude mcp add mcp-mcp uvx mcp-mcp

Usage Examples

Once configured, you can ask Claude Desktop to discover MCP servers using natural language:

• "Find me an MCP server for weather data"
• "I need a server for checking domain availability"
• "Search for MCP servers related to stock market data"
• "What MCP servers are available for web scraping?"

Development

Prerequisites

• Python 3.13+
• uv package manager
• direnv (optional, for automatic environment setup)
• just (optional, for convenient development commands)

Setup

# Clone the repository
git clone https://github.com/your-username/mcp-mcp.git
cd mcp-mcp

# Install dependencies
uv sync

# Run tests
uv run pytest

# Run the server
uv run main.py

Install via uvx (for testing)

For testing the installed package:

uvx mcp-mcp

This installs and runs the MCP-MCP server directly via uvx.

Development Commands (with justfile)

This project includes a justfile for common development tasks:

# List all available commands
just help

# Development with auto-reload
just dev      # STDIO mode with file watching
just dev-http # HTTP mode with file watching

# Running without auto-reload
just run-stdio # STDIO mode
just run-http  # HTTP mode

# Testing
just test             # Unit tests only
just test-integration # Include GitHub integration tests

# Building and publishing
just build           # Build package
just publish-test    # Publish to Test PyPI
just publish-prod    # Publish to Production PyPI

# Utilities
just version # Show version
just clean   # Clean build artifacts

Development Mode

For development and testing, use HTTP transport (easier to stop with Ctrl+C):

# HTTP mode (accessible at http://localhost:8000)
uv run main.py --http
# OR with justfile:
just run-http

# With auto-reload during development
just dev-http

# Custom host/port
uv run main.py --http --host 0.0.0.0 --port 3000

# STDIO mode (for MCP clients like Claude Desktop)
uv run main.py  # Note: To stop STDIO mode, use Ctrl+D (EOF), not Ctrl+C
# OR with justfile:
just run-stdio

# With auto-reload during development
just dev

Building

# Build package
uv build
# OR with justfile:
just build

# Test local installation
uvx --from ./dist/mcp_mcp-0.1.0-py3-none-any.whl mcp-mcp

Command Line Options

mcp-mcp --help

Option	Description	Default
--transport {stdio,http}	Transport method	stdio
--http	Use HTTP transport	-
--host HOST	Host for HTTP transport	localhost
--port PORT	Port for HTTP transport	8000

Testing

# Run all tests (unit + integration)
uv run pytest
# OR with justfile:
just test

# Run only unit tests (fast, no network)
uv run pytest db/ -v
# OR with justfile:
just test-unit

# Run only integration/e2e tests
uv run pytest tests/ -v
# OR with justfile:
just test-integration

# Run GitHub integration tests (optional, requires network)
MCP_MCP_TEST_GITHUB_INTEGRATION=1 uv run pytest tests/
# OR with justfile:
just test-integration-github

# Run all tests including GitHub integration
MCP_MCP_TEST_GITHUB_INTEGRATION=1 uv run pytest
# OR with justfile:
just test-all

# Run with coverage
uv run pytest --cov=db

Test Structure:

• Unit Tests: Located in db/ alongside the code they test (Go-style)
• Integration/E2E Tests: Located in tests/ directory

Integration Tests: Set MCP_MCP_TEST_GITHUB_INTEGRATION=1 to test real GitHub downloads and verify the complete first-user onboarding experience. These tests ensure users get fast startup (< 5 seconds) with 1,727+ servers.

Roadmap

Current Status: MVP Complete ✅

• ✅ Multi-source discovery (3 curated sources, 1,727+ unique servers)
• ✅ Semantic search with precomputed embeddings for sub-second response
• ✅ Production distribution via uvx/pipx with automated releases
• ✅ Security hardened with origin validation middleware
• ✅ Comprehensive test coverage (65+ tests)
• ✅ Complete documentation and development workflow

Future Enhancements (Beyond MVP)

• Docker integration for automatic server containerization
• MCP protocol proxy for seamless server execution
• GitHub API integration for live server discovery
• Server lifecycle management and cleanup
• Private registry support
• Dependency resolution
• Performance monitoring
• Web UI for server management

Contributing

We welcome contributions! Please see our development setup and:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Guidelines

• Follow Python 3.13+ best practices
• Add tests for new functionality
• Update documentation as needed
• Use semantic commit messages

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Model Context Protocol team at Anthropic
• Open source MCP server maintainers and contributors
• MCP Server Lists:
  • Official MCP Servers
  • Punkpeye's Awesome MCP Servers
  • Appcypher's Awesome MCP Servers

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Made with ❤️ for the MCP ecosystem