MCP Recommender

A smart MCP (Model Context Protocol) server that provides intelligent recommendations for other MCP servers based on your development needs.

Features

• 🔍 Smart Search: Find MCP servers using natural language queries

• 📊 Rich Database: Access to 874+ curated MCP servers across 36+ categories

• 🎯 Intelligent Matching: Advanced scoring algorithm for relevant recommendations

• 🏷️ Category Filtering: Filter by specific categories and programming languages

• 🚀 Easy Integration: Simple setup with uv package manager

• 🔧 Multiple Interfaces: Support for both CLI and MCP client integration

Related MCP server: MCP Registry Server

Installation

Using uv (Recommended)

# Clone the repository
git clone https://github.com/mcp-team/mcp-recommender.git
cd mcp-recommender

# Install with uv
uv sync

# Test the installation
uv run -m mcp_recommender --test

Using pip

pip install mcp-recommender

Usage

Command Line Interface

# Test mode - verify installation and see sample recommendations
uv run -m mcp_recommender --test

# Server mode - start the MCP server
uv run -m mcp_recommender --server

# Debug mode - detailed diagnostic information
uv run -m mcp_recommender --debug

MCP Client Integration

Add to your MCP client configuration:

{
  "mcpServers": {
    "mcp-recommender": {
      "isActive": true,
      "name": "mcp-recommender",
      "type": "stdio",
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/mcp-recommender",
        "run",
        "-m",
        "mcp_recommender"
      ]
    }
  }
}

Available Tools

Once integrated, you can use these tools in your MCP client:

recommend_mcp

Get intelligent MCP server recommendations based on your needs.

Parameters:

• query (string): Description of functionality you need

• limit (integer, optional): Maximum number of recommendations (default: 5)

• category (string, optional): Filter by specific category

• language (string, optional): Filter by programming language

Example:

recommend_mcp("database operations with SQLite", limit=3)

list_categories

List all available MCP categories with counts.

get_functional_keywords

Show functional keyword mappings for better search results.

Categories

The recommender covers 36+ categories including:

• Developer Tools (120+ servers)

• Databases (79+ servers)

• Search & Data Extraction (69+ servers)

• Cloud Platforms (39+ servers)

• Security (39+ servers)

• Communication (36+ servers)

• Browser Automation (23+ servers)

• Knowledge & Memory (22+ servers)

• And many more...

Development

Setup Development Environment

# Clone and setup
git clone https://github.com/mcp-team/mcp-recommender.git
cd mcp-recommender

# Install development dependencies
uv sync --dev

# Run tests
uv run pytest

# Build package
uv build

Project Structure

mcp-recommender/
├── mcp_recommender/           # Main package
│   ├── __init__.py
│   ├── __main__.py           # CLI entry point
│   ├── server.py             # MCP server implementation
│   └── data/                 # MCP database and keywords
│       ├── mcp_database.json
│       └── functional_keywords.json
├── tests/                    # Test suite
├── LICENSE                   # MIT License
├── README.md                 # This file
└── pyproject.toml           # Package configuration

Contributing

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

License

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

Acknowledgments

• Built with FastMCP framework

• MCP database curated from the awesome MCP community

• Powered by the Model Context Protocol

Support

• 📖 Documentation

• 🐛 Issue Tracker

• 💬 Discussions

Made with ❤️ by the MCP community