doITmagic/rag-code-mcp

RagCode MCP - Make Your Codebase AI-Ready

The privacy-first MCP server that transforms any repository into an AI-ready codebase with semantic search and RAG.

RagCode is a Model Context Protocol (MCP) server that instantly makes your project AI-ready. It enables AI assistants like GitHub Copilot, Cursor, Windsurf, and Claude to understand your entire codebase through semantic vector search, bridging the gap between your code and Large Language Models (LLMs).

Built with the official Model Context Protocol Go SDK, RagCode provides 9 powerful tools to index, search, and analyze code, making it the ultimate solution for AI-ready software development.

⚡ One-Command Installation

No Go, no build tools, no configuration needed. Just Docker.

curl -fsSL https://github.com/doITmagic/rag-code-mcp/releases/latest/download/rag-code-mcp_linux_amd64.tar.gz | tar xz && ./ragcode-installer -ollama=docker -qdrant=docker

curl -fsSL https://github.com/doITmagic/rag-code-mcp/releases/latest/download/rag-code-mcp_darwin_arm64.tar.gz | tar xz && ./ragcode-installer -ollama=docker -qdrant=docker

curl -fsSL https://github.com/doITmagic/rag-code-mcp/releases/latest/download/rag-code-mcp_darwin_amd64.tar.gz | tar xz && ./ragcode-installer -ollama=docker -qdrant=docker

Invoke-WebRequest -Uri "https://github.com/doITmagic/rag-code-mcp/releases/latest/download/rag-code-mcp_windows_amd64.zip" -OutFile "ragcode.zip"; Expand-Archive ragcode.zip -DestinationPath . -Force; .\ragcode-installer.exe -ollama=docker -qdrant=docker

That's it! The installer automatically:

• ✅ Downloads and installs the rag-code-mcp binary
• ✅ Sets up Ollama and Qdrant in Docker containers
• ✅ Downloads required AI models (phi3:medium, nomic-embed-text)
• ✅ Configures your IDE (VS Code, Cursor, Windsurf, Claude Desktop)
• ✅ Adds binaries to your PATH

📖 |

🎯 Zero-Config Usage

Once installed, you don't need to configure anything:

1. Open your project in your IDE (VS Code, Cursor, Windsurf)
2. Ask your AI assistant a question about your code
3. That's it! RagCode automatically indexes and answers

💬 "How does the authentication system work?"
💬 "Find all API endpoints in this codebase"
💬 "Show me the User model and its relationships"

First query triggers background indexing. Subsequent queries are instant.

📋 Table of Contents

🔒 Privacy-First: 100% Local AI

Your code never leaves your machine. RagCode runs entirely on your local infrastructure:

• ✅ Local AI Models - Uses Ollama for LLM and embeddings (runs on your hardware)
• ✅ Local Vector Database - Qdrant runs in Docker on your machine
• ✅ Zero Cloud Dependencies - No external API calls, no data transmission
• ✅ No API Costs - Free forever, no usage limits or subscriptions
• ✅ Offline Capable - Works without internet (after initial model download)

Perfect for: Enterprise codebases, proprietary projects, security-conscious teams.

🚀 Why RagCode? Performance Benefits

5-10x Faster Code Understanding

98% Token Savings

• Without RagCode: AI reads 5-10 files (~15,000 tokens) to find a function
• With RagCode: AI gets exact function + context (~200 tokens)

RagCode vs Cloud-Based Solutions

RagCode vs Generic RAG

🛠️ 9 Powerful MCP Tools

📖

🌐 Multi-Language Code Intelligence

Multi-Workspace Support

RagCode automatically detects and manages multiple workspaces with isolated indexes.

📖 - Auto-detection, stable IDs, caching

💻 IDE Integration

RagCode works with all major AI-powered IDEs:

📖 |

📦 System Requirements

Minimum Requirements

Recommended (for better performance)

📖

📚 Documentation

Getting Started

• - Install in 5 minutes
• - Manual IDE configuration

Configuration & Operations

• - Models, env vars, advanced settings
• - Common issues and solutions
• - Docker configuration details

Language Analyzers

• - Functions, types, interfaces, GoDoc
• - Classes, traits, PHPDoc
• - Eloquent, routes, controllers
• - Classes, decorators, type hints

Technical Reference

• - Technical deep dive
• - Complete API documentation
• - How smart indexing works
• - Multi-workspace support
• - Detailed Copilot setup

External Resources

• Model Context Protocol - Official MCP specification
• Ollama - Local LLM and embedding models
• Qdrant - Vector database

🤝 Contributing

We welcome contributions! Here's how you can help:

• 🐛 Report Bugs
• 💡 Request Features - Share ideas for new tools or languages
• 🔧 Submit PRs - Improve code, docs, or add features
• ⭐ Star the Project - Show your support

Development Setup

git clone https://github.com/doITmagic/rag-code-mcp.git
cd rag-code-mcp
go mod download
go run ./cmd/rag-code-mcp

📄 License

RagCode MCP is open source software licensed under the .

🏷️ Keywords & Topics

semantic-code-search rag retrieval-augmented-generation mcp-server model-context-protocol ai-code-assistant vector-search code-navigation ollama qdrant github-copilot cursor-ai windsurf go php laravel python django flask fastapi code-intelligence ast-analysis embeddings llm-tools local-ai privacy-first offline-ai self-hosted on-premise zero-cost no-cloud private-code-search enterprise-ai secure-coding-assistant