RAG Documentation MCP Server

# RAG Documentation MCP Server [](https://smithery.ai/server/@rahulretnan/mcp-ragdocs) An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. ## Table of Contents - [Features](#features) - [Quick Start](#quick-start) - [Docker Compose Setup](#docker-compose-setup) - [Web Interface](#web-interface) - [Configuration](#configuration) - [Cline Configuration](#cline-configuration) - [Claude Desktop Configuration](#claude-desktop-configuration) - [Acknowledgments](#acknowledgments) - [Troubleshooting](#troubleshooting) ## Features ### Tools 1. **search_documentation** - Search through the documentation using vector search - Returns relevant chunks of documentation with source information 2. **list_sources** - List all available documentation sources - Provides metadata about each source 3. **extract_urls** - Extract URLs from text and check if they're already in the documentation - Useful for preventing duplicate documentation 4. **remove_documentation** - Remove documentation from a specific source - Cleans up outdated or irrelevant documentation 5. **list_queue** - List all items in the processing queue - Shows status of pending documentation processing 6. **run_queue** - Process all items in the queue - Automatically adds new documentation to the vector store 7. **clear_queue** - Clear all items from the processing queue - Useful for resetting the system 8. **add_documentation** - Add new documentation to the processing queue - Supports various formats and sources ## Quick Start The RAG Documentation tool is designed for: - Enhancing AI responses with relevant documentation - Building documentation-aware AI assistants - Creating context-aware tooling for developers - Implementing semantic documentation search - Augmenting existing knowledge bases ## Docker Compose Setup The project includes a `docker-compose.yml` file for easy containerized deployment. To start the services: ```bash docker-compose up -d ``` To stop the services: ```bash docker-compose down ``` ## Web Interface The system includes a web interface that can be accessed after starting the Docker Compose services: 1. Open your browser and navigate to: `http://localhost:3030` 2. The interface provides: - Real-time queue monitoring - Documentation source management - Search interface for testing queries - System status and health checks ## Configuration ### Embeddings Configuration The system uses Ollama as the default embedding provider for local embeddings generation, with OpenAI available as a fallback option. This setup prioritizes local processing while maintaining reliability through cloud-based fallback. #### Environment Variables - `EMBEDDING_PROVIDER`: Choose the primary embedding provider ('ollama' or 'openai', default: 'ollama') - `EMBEDDING_MODEL`: Specify the model to use (optional) - For OpenAI: defaults to 'text-embedding-3-small' - For Ollama: defaults to 'nomic-embed-text' - `OPENAI_API_KEY`: Required when using OpenAI as provider - `FALLBACK_PROVIDER`: Optional backup provider ('ollama' or 'openai') - `FALLBACK_MODEL`: Optional model for fallback provider ### Cline Configuration Add this to your `cline_mcp_settings.json`: ```json { "mcpServers": { "rag-docs": { "command": "node", "args": ["/path/to/your/mcp-ragdocs/build/index.js"], "env": { "EMBEDDING_PROVIDER": "ollama", // default "EMBEDDING_MODEL": "nomic-embed-text", // optional "OPENAI_API_KEY": "your-api-key-here", // required for fallback "FALLBACK_PROVIDER": "openai", // recommended for reliability "FALLBACK_MODEL": "nomic-embed-text", // optional "QDRANT_URL": "http://localhost:6333" }, "disabled": false, "autoApprove": [ "search_documentation", "list_sources", "extract_urls", "remove_documentation", "list_queue", "run_queue", "clear_queue", "add_documentation" ] } } } ``` ### Claude Desktop Configuration Add this to your `claude_desktop_config.json`: ```json { "mcpServers": { "rag-docs": { "command": "node", "args": ["/path/to/your/mcp-ragdocs/build/index.js"], "env": { "EMBEDDING_PROVIDER": "ollama", // default "EMBEDDING_MODEL": "nomic-embed-text", // optional "OPENAI_API_KEY": "your-api-key-here", // required for fallback "FALLBACK_PROVIDER": "openai", // recommended for reliability "FALLBACK_MODEL": "nomic-embed-text", // optional "QDRANT_URL": "http://localhost:6333" } } } } ``` ### Default Configuration The system uses Ollama by default for efficient local embedding generation. For optimal reliability: 1. Install and run Ollama locally 2. Configure OpenAI as fallback (recommended): ```json { // Ollama is used by default, no need to specify EMBEDDING_PROVIDER "EMBEDDING_MODEL": "nomic-embed-text", // optional "FALLBACK_PROVIDER": "openai", "FALLBACK_MODEL": "text-embedding-3-small", "OPENAI_API_KEY": "your-api-key-here" } ``` This configuration ensures: - Fast, local embedding generation with Ollama - Automatic fallback to OpenAI if Ollama fails - No external API calls unless necessary Note: The system will automatically use the appropriate vector dimensions based on the provider: - Ollama (nomic-embed-text): 768 dimensions - OpenAI (text-embedding-3-small): 1536 dimensions ## Acknowledgments This project is a fork of [qpd-v/mcp-ragdocs](https://github.com/qpd-v/mcp-ragdocs), originally developed by qpd-v. The original project provided the foundation for this implementation. Special thanks to the original creator, qpd-v, for their innovative work on the initial version of this MCP server. This fork has been enhanced with additional features and improvements by Rahul Retnan. ## Troubleshooting ### Server Not Starting (Port Conflict) If the MCP server fails to start due to a port conflict, follow these steps: 1. Identify and kill the process using port 3030: ```bash npx kill-port 3030 ``` 2. Restart the MCP server 3. If the issue persists, check for other processes using the port: ```bash lsof -i :3030 ``` 4. You can also change the default port in the configuration if needed