Make it easy for large models to read code repositories: MCP-Repo2LLM

An efficient tool that seamlessly integrates traditional code bases and AI language models.
Core content:
1. MCP-Repo2LLM design concept and function overview
2. Solutions to core issues in the interaction between code repositories and LLM
3. Installation method and core tool function introduction
MCP-Repo2LLM
Download
https://github.com/crisschan/mcp-repo2llm
Overview
mcp-repo2llm is an MCP server that converts code repositories into an efficient format suitable for large language model (LLM) processing. It is a powerful tool that can seamlessly connect traditional code bases with modern AI language models, helping developers better utilize artificial intelligence technology. This tool is developed based on RepoToTextForLLMs , which provides the core functionality of converting code repositories into LLM readable format.
Start the engine
As artificial intelligence and large language models (LLMs) become increasingly important in software development, it is urgent to make these models better understand our code base. The traditional code repository structure is not optimized for LLMs, which leads to poor results when using AI tools for code analysis or generation, making it difficult to fully realize their potential.
Problems solved
This project aims to solve the following key problems:
Difficulties of LLM with large code bases It is easy to lose context and structure when feeding code into AI models Inefficient warehouse metadata and document processing The problem of inconsistent formats between different programming languages
Core Features
Smart repository scanning : can intelligently process the entire code base while maintaining its structural integrity Context Preservation : Preserve important context information and associations between code files Multi-language support : supports multiple programming languages and optimizes processing for different languages Metadata enhancement : Add relevant metadata to the code to improve LLM's understanding ability Efficient processing : Optimized design, suitable for processing large warehouses, with extremely low resource consumption
Installation Method
The steps to install mcp-repo2llm via uv are as follows:
"mcp-repo2llm-server" : {
"command" : "uv" ,
"args" : [
"run" ,
"--with" ,
"mcp[cli]" ,
"--with-editable" ,
"/mcp-repo2llm" ,
"mcp" ,
"run" ,
"/mcp-repo2llm/mcp-repo2llm-server.py"
],
"env" : {
"GITHUB_TOKEN" : "your-github-token" ,
"GITLAB_TOKEN" : "your-gitlab-token"
}
}
GITHUB_TOKEN : Your GitHub access token GITLAB_TOKEN : Your GitLab access token
Tool Introduction
get_gitlab_repo
Function : Process and return the code content of a branch of the GitLab repository and output it in text form Input parameters : repo_url
(String): URL of the GitLab repositorybranch
(string): Branch name, default is masterReturn value (string): All project information and structure in the repository, presented in text form
get_github_repo
Function : Process and return the code content of a branch of the GitHub repository and output it in text form Input parameters : repo_url
(String): URL of the GitHub repositorybranch
(string): Branch name, default is masterReturn value (string): All project information and structure in the repository, presented in text form
get_local_repo
Function : Process and return the code content of the local warehouse and output it in text form Input parameters : repo_url
(String): Path to the local repositoryReturn value (string): All project information and structure in the repository, presented in text form