Allows analyzing Git-based codebases to generate structured summaries and contextual prompts for AI assistants to better understand repositories
Enables working with GitHub repositories by extracting relevant code context and producing optimized summaries for AI consumption
Leverages the code2prompt-rs Rust library to analyze codebases and produce structured summaries optimized for AI assistants
An MCP server that generates contextual prompts from codebases, making it easier for AI assistants to understand and work with your code repositories.
code2prompt-mcp leverages the high-performance code2prompt-rs Rust library to analyze codebases and produce structured summaries. It helps bridge the gap between your code and language models by extracting relevant context in a format that's optimized for AI consumption.
This project uses Rye for dependency management, make sure you have it installed.
To install the necessary dependencies, and build the module in the local environment, run:
It will install all the required dependencies specified in the pyproject.toml file in the .venv directory.
Run the MCP server:
MIT License - See LICENSE file for details.
For testing, you can use the MCP Inspector:
This server cannot be installed
An MCP server that analyzes codebases and generates contextual prompts, making it easier for AI assistants to understand and work with code repositories.
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