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

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Provides intelligent recommendations for MCP servers based on development needs using natural language queries. Searches through 874+ curated MCP servers across 36+ categories with advanced matching algorithms.

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MCP Recommender

MCP Recommender

Features

Installation

Using uv (Recommended)

Using pip

Usage

Command Line Interface

MCP Client Integration

Available Tools

`get_functional_keywords`

Categories

Development

Setup Development Environment

Project Structure

Contributing

License

Acknowledgments

Support

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DARPEngine

MCPfinder Server

Rini MCP Server

New MCP Servers

MCP directory API