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Mcp Mcp

Mcp Mcp

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github.com/wojtyniakAI & Agents23Updated 18w ago

About Mcp Mcp

MCP-MCP is an MCP server directory for AI assistants to discover other MCP servers. It searches and recommends entries aggregated from curated sources, using semantic search and supporting STDIO and HTTP transports.

Key features

  1. 01Aggregate MCP server catalogs from multiple sources
  2. 02Search and recommend servers by need
  3. 03Refresh and deduplicate catalog data
  4. 04Support STDIO and HTTP transports

Use cases

  1. 01Find an MCP server for weather data
  2. 02Search for domain availability tools
  3. 03Discover servers for stock data or web scraping

MCP support

It exposes MCP server discovery and recommendation to MCP clients. Clients can search the catalog using natural-language descriptions of their needs.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"mcp": {
"command": "uvx",
"args": [
"mcp-mcp"
]
}
}
}
Run commanduvx mcp-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypimcp-mcp

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP-MCP: Meta-MCP Server

Servers Python 3.13+ License: MIT Build Status PyPI

MCP-MCP: An MCP server that helps discover other MCP servers

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.

🗃️ 2,874+ 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
OptionDescriptionDefault
--transport {stdio,http}Transport methodstdio
--httpUse HTTP transport-
--host HOSTHost for HTTP transportlocalhost
--port PORTPort for HTTP transport8000

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 2,874+ servers.

Roadmap

Current Status: MVP Complete ✅
  • ✅ Multi-source discovery (3 curated sources, 2,874+ 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


Made with ❤️ for the MCP ecosystem

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.