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

Written by
Audrey Miles
Updated on:June-30th-2025
Recommendation

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

Yang Fangxian
Founder of 53AI/Most Valuable Expert of Tencent Cloud (TVP)

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 repository
    • branch(string): Branch name, default is master
  • Return 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 repository
    • branch(string): Branch name, default is master
  • Return 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 repository
  • Return value (string): All project information and structure in the repository, presented in text form