About MCP-RAG
MCP-RAG is an MCP server that gives AI assistants real-time access to relevant documentation through semantic vector search. It is intended for assistants that need documentation-grounded context for responses.
Key features
- 01Semantic vector search
- 02Real-time documentation access
- 03Context-aware documentation retrieval
Use cases
- 01Grounding assistant responses in documentation
- 02Retrieving relevant documentation for AI models
MCP support
Exposes documentation retrieval through semantic vector search to MCP clients, enabling AI assistants to access relevant information.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP-RAG
The MCP-RAG Server is an implementation of the Model Context Protocol (MCP) designed to enhance AI assistants by providing them with real-time access to relevant documentation through semantic vector search. It enables AI models to retrieve and process documentation, allowing for context-aware responses that are grounded in up-to-date information
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.