mcp server

mcp-recommend

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

bxzymy/mcp-recommendcommunitylistedunreviewed

Use tool

Set up

Runs on your computer. Pick your agent and operating system.

Before you start

uv (`curl -LsSf https://astral.sh/uv/install.sh | sh`)

~/Library/Application Support/Claude/claude_desktop_config.json

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

same format Cursor, GitHub Copilot CLI, Junie, JetBrains AI Assistant, Augment (Auggie CLI), Factory Droid, Kiro, Devin CLI +12

Values in angle brackets are placeholders. Restart Claude Desktop after editing its config.Claude Desktop docs

README

synced Oct 26, 2026

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

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.

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

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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

Made with ❤️ by the MCP community