RAG Documentation MCP Server is an implementation of an MCP server that provides tools for retrieving and processing documentation using vector search. It enables AI assistants to augment their responses with relevant documentation context, making it ideal for enhancing AI-driven interactions with structured knowledge.
This server is designed to enhance AI responses by integrating relevant documentation, build documentation-aware AI assistants, create context-aware tooling for developers, implement semantic documentation search, and augment existing knowledge bases. Its features include vector-based document search, source management, queue handling, and more.
The project is a fork of qpd-v/mcp-ragdocs originally developed by qpd-v. It has been enhanced with additional features and improvements by Rahul Retnan.
The server can be deployed locally or in containerized environments using Docker Compose. The provided `docker-compose.yml` file simplifies deployment, and the web interface can be accessed via `http://localhost:3030` after starting the services.
The initial release (v0.1.0) occurred on December 13, 2024. Updates such as version 1.1.0 were released earlier on March 14, 2024.
By default, the system uses Ollama for local embeddings generation, with OpenAI available as a fallback. You can configure the embedding provider, model, and fallback options through environment variables like `EMBEDDING_PROVIDER`, `EMBEDDING_MODEL`, and `OPENAI_API_KEY`.
The server includes tools such as `search_documentation` (for vector-based searches), `list_sources` (to view available documentation sources), `extract_urls` (to prevent duplicate entries), `remove_documentation`, `list_queue`, `run_queue`, `clear_queue`, and `add_documentation`.
If the server fails to start due to a port conflict, you can kill the process using port 3030 with `npx kill-port 3030`. Alternatively, check for conflicting processes with `lsof -i :3030` or change the default port in the configuration.
Yes, you can configure the server for Claude Desktop by adding the appropriate settings to your `claude_desktop_config.json` file. This includes specifying the command, arguments, and environment variables like `EMBEDDING_PROVIDER` and `OPENAI_API_KEY`.
The web interface allows users to monitor the processing queue in real-time, manage documentation sources, test queries via a search interface, and check the system's status and health.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.