Repo Radar MCP
Discover, rank, and compare GitHub repositories from any MCP-compatible AI client. Enables searching, filtering, ranking, and evaluating open-source repositories by topic, language, stars, license, activity, and relevance.
README
Repo Radar MCP
Discover, rank and compare GitHub repositories from any MCP-compatible AI client.
Repo Radar MCP is a Python-based MCP server that connects AI agents to the GitHub API, allowing them to search, filter, rank and evaluate open-source repositories by topic, language, stars, license, activity and relevance.
It is designed for developers, builders and AI agents that need a structured way to research open-source projects.
Why Repo Radar MCP?
AI agents can write code, but they also need good technical context.
Repo Radar MCP gives your MCP-compatible client a simple set of tools to answer questions like:
- What are the most popular Python repositories about RAG?
- Which MCP servers are worth studying?
- What GitHub projects are active, licensed and useful?
- Which repository should I use as a reference for my next project?
- How do several repositories compare by stars, forks, issues, license and recent activity?
Features
- Search GitHub repositories by topic.
- Filter by programming language.
- Filter by minimum stars.
- Rank repositories with a simple usefulness score.
- Analyze a single repository.
- Compare multiple repositories.
- Fetch repository README content.
- Return results as JSON or Markdown.
- Uses your GitHub token safely from environment variables.
- Works with MCP Inspector, Claude Desktop, Cursor and other MCP-compatible clients.
MCP Tools
| Tool | Description |
|---|---|
search_repositories |
Search GitHub repositories by topic, language, stars and sorting mode. |
search_repositories_markdown |
Same as above, but returns a clean Markdown report. |
rank_repositories |
Search repositories and add a practical repository score. |
rank_repositories_markdown |
Search, rank and return repositories as Markdown. |
analyze_repository |
Analyze one repository by owner/name. |
analyze_repository_markdown |
Analyze one repository and return a Markdown report. |
compare_repositories |
Compare several repositories by owner/name. |
compare_repositories_markdown |
Compare several repositories and return a Markdown table. |
get_repository_readme |
Fetch the README of a repository. |
Installation
Clone the repository:
git clone https://github.com/javiermorron/repo-radar-mcp.git
cd repo-radar-mcp
Create and activate a virtual environment.
Windows PowerShell
python -m venv .venv
.\.venv\Scripts\activate
macOS / Linux
python -m venv .venv
source .venv/bin/activate
Install the project:
pip install -e .
Copy the environment file:
cp .env.example .env
On Windows PowerShell:
copy .env.example .env
Edit .env and add your GitHub token:
GITHUB_TOKEN=your_github_token_here
GITHUB_API_VERSION=2022-11-28
GITHUB_USER_AGENT=repo-radar-mcp/1.0.0
Never commit your real .env file.
Run with MCP Inspector
From the project root:
mcp dev server.py
If the Inspector does not find uv, use this configuration in the Inspector:
Transport Type: STDIO
Command: python
Arguments: server.py
Then open the Tools tab and run:
{
"topic": "mcp server",
"language": "Python",
"limit": 5,
"min_stars": 10
}
Claude Desktop Example
A sample configuration is available in:
examples/claude_desktop_config.example.json
Example Windows entry:
{
"mcpServers": {
"repo-radar-mcp": {
"command": "C:\\Users\\YOUR_USER\\repo-radar-mcp\\.venv\\Scripts\\python.exe",
"args": [
"C:\\Users\\YOUR_USER\\repo-radar-mcp\\server.py"
]
}
}
}
Example Prompts
Search the 5 most popular Python repositories about "mcp server" and explain which one is best to study.
Compare these repositories: modelcontextprotocol/python-sdk, langchain-ai/langchain, run-llama/llama_index.
Find popular repositories about "rag assistant" in Python with more than 500 stars and rank them by usefulness.
Analyze microsoft/autogen and tell me if it is active, useful and worth studying.
More prompts are available in:
examples/prompts.md
Repository Score
Repo Radar MCP includes a simple scoring system based on:
- Stars
- Forks
- Recent activity
- License availability
- Open issues
- Archived status
The score is not meant to replace human judgment. It is a quick signal to help agents and developers prioritize what to inspect first.
Project Structure
repo-radar-mcp/
|-- src/
| |-- repo_radar_mcp/
| | |-- __init__.py
| | |-- server.py
| | |-- github_client.py
| | |-- scoring.py
| | |-- formatters.py
| | |-- models.py
|-- examples/
| |-- claude_desktop_config.example.json
| |-- prompts.md
|-- tests/
| |-- test_scoring.py
|-- .env.example
|-- .gitignore
|-- LICENSE
|-- README.md
|-- CHANGELOG.md
|-- requirements.txt
|-- pyproject.toml
|-- server.py
Roadmap
- Add repository release analysis.
- Add issue quality analysis.
- Add contributor activity metrics.
- Add support for GitHub topics recommendations.
- Add repository health report.
- Add CSV and JSON export helpers.
- Add Docker support.
- Add GitHub Actions for tests and linting.
Security
Repo Radar MCP uses a GitHub token from environment variables.
Do not commit:
.env- Personal access tokens
- Private API keys
- Local virtual environments
The .gitignore file already excludes common sensitive and generated files.
Contributing
Contributions are welcome.
Good first issues:
- Improve scoring logic.
- Add more output formats.
- Add tests.
- Improve MCP client examples.
- Add Docker support.
License
This project is licensed under the MIT License.
Created by Javier Morrón. Connect with me on LinkedIn: https://www.linkedin.com/in/javiermorron.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
E2B
Using MCP to run code via e2b.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.