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.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。