Rag Code

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Privacy-first semantic code navigation MCP server using RAG. Features deep AST multi-language support (Go, PHP/Laravel/WP, JS/TS/React, Python), 100% local LLMs (Ollama), and vector search (Qdrant) fo

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1# Rag Code

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Stars: 55
Tools: —
Uptime: —

Overview

About Rag Code

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Rag Code?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Rag Code need an API key?

No required environment variable is listed on this record.

What transport does Rag Code use?

Rag Code runs locally over stdio.

When was Rag Code last checked?

Last checked 2026-10-28. Privacy-first semantic code navigation MCP server using RAG. Features deep AST multi-language support (Go, PHP/Laravel/WP, JS/TS/React, Python), 100% local LLMs (Ollama), and vector search (Qdrant) for AI IDEs like Antigravity, Cursor, Windsurf, Copilot, and Claude.

From the publisher

Documentation

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.

Linux (amd64)

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

macOS (Apple Silicon / Intel)

# Apple Silicon (M1/M2/M3)
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

# Intel Macs
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

Windows (PowerShell)

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, mxbai-embed-large)
✅ Configures your IDE (VS Code, Cursor, Windsurf, Claude Desktop)
✅ Adds binaries to your PATH

🔄 Keep Updated

The auto-update feature is available starting with v1.1.18.

How to Upgrade:

If you are on an older version (< v1.1.18): Your version does not know the update command. You must re-run the installation command (the curl command above) one last time. (Don't worry, your indexes and configuration will be preserved).
If you are on v1.1.18 or newer: Simply run:
```
rag-code-mcp --update
```

For new installations: The update system is built-in. Use the installer once, then simply run rag-code-mcp --update anytime to get the latest features.

📖 Full Installation Guide → | Windows WSL Setup →

🎯 Zero-Config Usage

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

Open your project in your IDE (VS Code, Cursor, Windsurf)
Ask your AI assistant a question about your code
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

Section	Description
🔒 Privacy & Security	100% local, zero cloud dependencies
🚀 Why RagCode?	Performance benefits, comparisons
🛠️ MCP Tools	All 9 tools explained
🌐 Supported Languages	Go, PHP, Python support
💻 IDE Integration	Windsurf, Cursor, VS Code, Claude
⚙️ Configuration	Advanced settings, models, env vars
🐛 Troubleshooting	Common issues and solutions
📚 Documentation	All guides and references

🔒 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

Task	Without RagCode	With RagCode	Speedup
Find authentication logic	30-60s (read 10+ files)	2-3s (semantic search)	10-20x
Understand function signature	15-30s (grep + read)	1-2s (direct lookup)	15x
Find all API endpoints	60-120s (manual search)	3-5s (hybrid search)	20-40x

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

Feature	RagCode (Local)	Cloud AI Search
Privacy	✅ 100% local	❌ Code sent to cloud
Cost	✅ $0 forever	❌ $20-100+/month
Offline	✅ Works offline	❌ Requires internet
Data Control	✅ You own everything	❌ Vendor controls data

RagCode vs Generic RAG

Aspect	Generic RAG	RagCode
Chunking	Arbitrary text splits	Semantic units (functions, classes)
Metadata	Filename only	Name, type, params, dependencies, lines
Results	May return partial code	Always complete, runnable code

🛠️ 9 Powerful MCP Tools

Tool	Description	Use When
`search_code`	Semantic search by meaning	First choice for exploration
`hybrid_search`	Keyword + semantic for exact matches	Need exact identifiers
`get_function_details`	Complete function source code	Know exact function name
`find_type_definition`	Type/class with fields and methods	Understand data models
`find_implementations`	All usages and callers	Before refactoring
`list_package_exports`	All exported symbols	Explore unfamiliar packages
`search_docs`	Search Markdown documentation	Setup, architecture info
`get_code_context`	Code snippet with context	Have file:line reference
`index_workspace`	Reindex codebase	After major changes

📖 Full Tool Reference →

🌐 Multi-Language Code Intelligence

Language	Support Level	Features	Docs
Go	✅ Full	Functions, types, interfaces, methods, AST analysis	📖 Go Analyzer
PHP	✅ Full	Classes, methods, interfaces, traits, PHPDoc	📖 PHP Analyzer
PHP + Laravel	✅ Full	Eloquent models, routes, controllers, middleware	📖 Laravel Analyzer
Python	✅ Full	Classes, functions, decorators, type hints, mixins	📖 Python Analyzer
JavaScript/TypeScript	🔜 Planned	Coming soon (tree-sitter based)	-

Multi-Workspace Support

RagCode automatically detects and manages multiple workspaces with isolated indexes.

📖 Workspace Detection → - Auto-detection, stable IDs, caching

💻 IDE Integration

RagCode works with all major AI-powered IDEs:

IDE	Status	Setup
Windsurf	✅ Auto-configured	Just install
Cursor	✅ Auto-configured	Just install
VS Code + Copilot	✅ Auto-configured	Requires VS Code 1.95+
Claude Desktop	✅ Auto-configured	Just install
Antigravity	✅ Auto-configured	Just install

📖 Manual IDE Setup → | VS Code + Copilot Guide →

📦 System Requirements

Minimum Requirements

Component	Requirement	Notes
CPU	4 cores	For running Ollama models
RAM	16 GB	8 GB for `phi3:medium`, 4 GB for `mxbai-embed-large`, 4 GB system
Disk	10 GB free	~8 GB for models + 2 GB for data
OS	Linux, macOS, Windows	Docker required for Qdrant

Recommended (for better performance)

Component	Requirement	Notes
CPU	8+ cores	Better concurrent operations
RAM	32 GB	Comfortable multi-workspace indexing
GPU	NVIDIA 8GB+ VRAM	Significantly speeds up Ollama (optional)
Disk	20 GB SSD	Faster indexing and search

📖 Full Requirements →

📚 Documentation

Getting Started

Quick Start Guide - Install in 5 minutes
IDE Setup - Manual IDE configuration

Configuration & Operations

Configuration Guide - Models, env vars, advanced settings
Troubleshooting - Common issues and solutions
Docker Setup - Docker configuration details

Language Analyzers

Go Analyzer - Functions, types, interfaces, GoDoc
PHP Analyzer - Classes, traits, PHPDoc
Laravel Analyzer - Eloquent, routes, controllers
Python Analyzer - Classes, decorators, type hints

Technical Reference

Architecture Overview - Technical deep dive
Tool Schema Reference - Complete API documentation
Incremental Indexing - How smart indexing works
Workspace Detection - Multi-workspace support
VS Code + Copilot - 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 MIT License.

🏷️ 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

Built with ❤️ for developers who want smarter AI code assistants

⭐ Star us on GitHub if RagCode helps your workflow!

Built and maintained by DO IT MAGIC SOFTWARE, custom software and applied AI from Pitești, Romania.

Questions? Open an Issue • Read the Docs • Join Discussions

Rag Code

doITmagic55starsClaim this server

Knowledge Memory

Install Run on PlugMCPClaim this server

GitHub Website

Run on PlugMCP

1# Rag Code

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Stars: 55
Tools: —
Uptime: —

Overview

About Rag Code

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Rag Code?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Rag Code need an API key?

No required environment variable is listed on this record.

What transport does Rag Code use?

Rag Code runs locally over stdio.

When was Rag Code last checked?

From the publisher

Documentation

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.

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.

Linux (amd64)

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

macOS (Apple Silicon / Intel)

# Apple Silicon (M1/M2/M3)
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

# Intel Macs
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

Windows (PowerShell)

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, mxbai-embed-large)
✅ Configures your IDE (VS Code, Cursor, Windsurf, Claude Desktop)
✅ Adds binaries to your PATH

🔄 Keep Updated

The auto-update feature is available starting with v1.1.18.

How to Upgrade:

If you are on an older version (< v1.1.18): Your version does not know the update command. You must re-run the installation command (the curl command above) one last time. (Don't worry, your indexes and configuration will be preserved).
If you are on v1.1.18 or newer: Simply run:
```
rag-code-mcp --update
```

For new installations: The update system is built-in. Use the installer once, then simply run rag-code-mcp --update anytime to get the latest features.

📖 Full Installation Guide → | Windows WSL Setup →

🎯 Zero-Config Usage

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

Open your project in your IDE (VS Code, Cursor, Windsurf)
Ask your AI assistant a question about your code
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

Section	Description
🔒 Privacy & Security	100% local, zero cloud dependencies
🚀 Why RagCode?	Performance benefits, comparisons
🛠️ MCP Tools	All 9 tools explained
🌐 Supported Languages	Go, PHP, Python support
💻 IDE Integration	Windsurf, Cursor, VS Code, Claude
⚙️ Configuration	Advanced settings, models, env vars
🐛 Troubleshooting	Common issues and solutions
📚 Documentation	All guides and references

🔒 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

Task	Without RagCode	With RagCode	Speedup
Find authentication logic	30-60s (read 10+ files)	2-3s (semantic search)	10-20x
Understand function signature	15-30s (grep + read)	1-2s (direct lookup)	15x
Find all API endpoints	60-120s (manual search)	3-5s (hybrid search)	20-40x

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

Feature	RagCode (Local)	Cloud AI Search
Privacy	✅ 100% local	❌ Code sent to cloud
Cost	✅ $0 forever	❌ $20-100+/month
Offline	✅ Works offline	❌ Requires internet
Data Control	✅ You own everything	❌ Vendor controls data

RagCode vs Generic RAG

Aspect	Generic RAG	RagCode
Chunking	Arbitrary text splits	Semantic units (functions, classes)
Metadata	Filename only	Name, type, params, dependencies, lines
Results	May return partial code	Always complete, runnable code

🛠️ 9 Powerful MCP Tools

Tool	Description	Use When
`search_code`	Semantic search by meaning	First choice for exploration
`hybrid_search`	Keyword + semantic for exact matches	Need exact identifiers
`get_function_details`	Complete function source code	Know exact function name
`find_type_definition`	Type/class with fields and methods	Understand data models
`find_implementations`	All usages and callers	Before refactoring
`list_package_exports`	All exported symbols	Explore unfamiliar packages
`search_docs`	Search Markdown documentation	Setup, architecture info
`get_code_context`	Code snippet with context	Have file:line reference
`index_workspace`	Reindex codebase	After major changes

📖 Full Tool Reference →

🌐 Multi-Language Code Intelligence

Language	Support Level	Features	Docs
Go	✅ Full	Functions, types, interfaces, methods, AST analysis	📖 Go Analyzer
PHP	✅ Full	Classes, methods, interfaces, traits, PHPDoc	📖 PHP Analyzer
PHP + Laravel	✅ Full	Eloquent models, routes, controllers, middleware	📖 Laravel Analyzer
Python	✅ Full	Classes, functions, decorators, type hints, mixins	📖 Python Analyzer
JavaScript/TypeScript	🔜 Planned	Coming soon (tree-sitter based)	-

Multi-Workspace Support

RagCode automatically detects and manages multiple workspaces with isolated indexes.

📖 Workspace Detection → - Auto-detection, stable IDs, caching

💻 IDE Integration

RagCode works with all major AI-powered IDEs:

IDE	Status	Setup
Windsurf	✅ Auto-configured	Just install
Cursor	✅ Auto-configured	Just install
VS Code + Copilot	✅ Auto-configured	Requires VS Code 1.95+
Claude Desktop	✅ Auto-configured	Just install
Antigravity	✅ Auto-configured	Just install

📖 Manual IDE Setup → | VS Code + Copilot Guide →

📦 System Requirements

Minimum Requirements

Component	Requirement	Notes
CPU	4 cores	For running Ollama models
RAM	16 GB	8 GB for `phi3:medium`, 4 GB for `mxbai-embed-large`, 4 GB system
Disk	10 GB free	~8 GB for models + 2 GB for data
OS	Linux, macOS, Windows	Docker required for Qdrant

Recommended (for better performance)

Component	Requirement	Notes
CPU	8+ cores	Better concurrent operations
RAM	32 GB	Comfortable multi-workspace indexing
GPU	NVIDIA 8GB+ VRAM	Significantly speeds up Ollama (optional)
Disk	20 GB SSD	Faster indexing and search

📖 Full Requirements →

📚 Documentation

Getting Started

Quick Start Guide - Install in 5 minutes
IDE Setup - Manual IDE configuration

Configuration & Operations

Configuration Guide - Models, env vars, advanced settings
Troubleshooting - Common issues and solutions
Docker Setup - Docker configuration details

Language Analyzers

Go Analyzer - Functions, types, interfaces, GoDoc
PHP Analyzer - Classes, traits, PHPDoc
Laravel Analyzer - Eloquent, routes, controllers
Python Analyzer - Classes, decorators, type hints

Technical Reference

Architecture Overview - Technical deep dive
Tool Schema Reference - Complete API documentation
Incremental Indexing - How smart indexing works
Workspace Detection - Multi-workspace support
VS Code + Copilot - 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 MIT License.

🏷️ Keywords & Topics

Built with ❤️ for developers who want smarter AI code assistants

⭐ Star us on GitHub if RagCode helps your workflow!

Built and maintained by DO IT MAGIC SOFTWARE, custom software and applied AI from Pitești, Romania.

Questions? Open an Issue • Read the Docs • Join Discussions