MCP-server-ragdocs
by sanderkooger
An MCP server implementation that provides tools for retrieving and processing documentation through vector search. It enables AI assistants to augment their responses with relevant documentation context.
Last updated: N/A
What is MCP-server-ragdocs?
MCP-server-ragdocs is a server that leverages vector search to retrieve and process documentation, allowing AI assistants to enhance their responses with relevant context. It supports multiple documentation sources and embedding providers like Ollama and OpenAI.
How to use MCP-server-ragdocs?
Configure the server with your desired embedding provider (Ollama or OpenAI) and Qdrant vector database. Add documentation sources, and use the provided tools (search_documentation, list_sources, etc.) to manage and query the documentation. Integrate the server with your AI assistant to augment its responses with retrieved documentation.
Key features of MCP-server-ragdocs
Vector-based documentation search and retrieval
Support for multiple documentation sources
Support for local (Ollama) embeddings generation or OPENAI
Semantic search capabilities
Automated documentation processing
Real-time context augmentation for LLMs
Use cases of MCP-server-ragdocs
Enhancing AI responses with relevant documentation
Building documentation-aware AI assistants
Creating context-aware tooling for developers
Implementing semantic documentation search
FAQ from MCP-server-ragdocs
What is the purpose of this server?
What is the purpose of this server?
To provide tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
What embedding providers are supported?
What embedding providers are supported?
Ollama and OpenAI are supported for generating embeddings.
What is Qdrant?
What is Qdrant?
Qdrant is a vector database used to store and search the documentation embeddings.
How do I configure the server?
How do I configure the server?
Configuration is done via environment variables, specifying the embedding provider, API keys (if applicable), and Qdrant URL.
What tools are available?
What tools are available?
Tools include search_documentation, list_sources, extract_urls, remove_documentation, list_queue, run_queue, and clear_queue.