MCP-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
what is MCP-RAG?
MCP-RAG is a server implementation of the Model Context Protocol (MCP) that enhances AI assistants by providing them with real-time access to relevant documentation through semantic vector search.
how to use MCP-RAG?
To use MCP-RAG, integrate it with your AI assistant to enable it to retrieve and process documentation, allowing for context-aware responses based on up-to-date information.
key features of MCP-RAG?
- Real-time access to relevant documentation
- Semantic vector search for improved retrieval
- Context-aware responses for AI models
use cases of MCP-RAG?
- Enhancing customer support AI with up-to-date documentation.
- Improving educational AI tools by providing real-time access to learning materials.
- Enabling research assistants to retrieve the latest studies and papers.
FAQ from MCP-RAG?
- Can MCP-RAG be used with any AI model?
Yes! MCP-RAG is designed to be compatible with various AI models that require access to documentation.
- Is MCP-RAG open-source?
Yes! MCP-RAG is available on GitHub for anyone to use and contribute.
- How does semantic vector search work?
Semantic vector search uses advanced algorithms to understand the context of queries and retrieve the most relevant documents.
MCP-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