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MCP Recommender

by bxzymy
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Python
Hybrid

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: DARPEngine

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

Support

Made with ❤️ by the MCP community

One-click Deploy
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

Resources

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