GitHub Repository Assistant MCP Server

@deploypilotorg

代码仓库

基本信息

分类

版本控制

传输方式

stdio

作者

deploypilotorg

关于 GitHub Repository Assistant MCP Server

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GitHub Repository Assistant MCP Server 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

GitHub Repository Assistant MCP Server 工具

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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is GitHub Repository Assistant?

GitHub Repository Assistant is an MCP server that connects Claude with GitHub repositories, enabling code analysis and task assistance like creating deployment workflows. It is designed for developers who want to interact with repositories through Claude.

How to use GitHub Repository Assistant?

Install Python 3.13+, Git, and set your Anthropic API key in a .env file. Install dependencies with pip install -r requirements.txt, then run streamlit run app.py. In the sidebar, enter the path to server.py and click "Connect to Server". Enter a GitHub repository URL and click "Analyze Repository" to start interacting.

Key features of GitHub Repository Assistant

Clone and analyze any GitHub repository
Interact with repositories through Claude 3.5 Sonnet
Request deployment workflows and code generation tasks
Seamless integration via Streamlit frontend
MCP server with specialized tools for repository interaction

Use cases of GitHub Repository Assistant

Understand what files exist in a repository and their purposes
Analyze specific source files like main.py for functionality
Generate a GitHub Actions workflow for deployment
Create a Dockerfile for an application

常见问题

What types of repositories are supported?

Only public GitHub repositories are currently supported.

What are the system requirements?

Python 3.13 or higher, Git installed and available in PATH, and an Anthropic API key.

How do I connect the MCP server in the Streamlit app?

In the sidebar, enter the path to the `server.py` file in the "Server Script Path" field and click "Connect to Server".

Are there any limitations on repository size?

Large repositories may take longer to clone and analyze.

Do generated workflows work immediately?

Generated deployment workflows may need manual adjustments based on specific requirements.

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