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Connect to a multimodal RAG service for hybrid document search and Q&A.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "rag-mcp" yet — see the docs or source repo.
Use rag-mcp to search the knowledge base for “supply chain risk management,” then return the most relevant points and a brief answer.
A summary of relevant search results and a brief answer grounded in the documents.
Use rag-mcp to answer from indexed documents: What hybrid retrieval methods does our product support? If the documents do not mention it, say so clearly.
A document-grounded answer, or a clear note that no supporting evidence was found.
Call the health-check tool in rag-mcp to verify whether the RAG service is currently available and briefly report the result.
A health status result indicating whether the service is available, useful for troubleshooting connectivity or runtime issues.
Developers or researchers can use it as an MCP bridge to connect a multimodal RAG service, run hybrid search over documents, and get question-answering results. It fits workflows where teams need to quickly locate information in a knowledge base.
When an AI assistant needs access to a document knowledge base, this tool can issue queries and return document-grounded answers. This helps integrate external retrieval capabilities into existing agent workflows.
DevOps engineers or developers can use the health-check tool to verify whether the backend RAG service is responding normally. It is useful during integration, post-deployment validation, or incident troubleshooting.
It is an MCP tool that serves as a bridge to a multimodal RAG service. The provided information says it supports hybrid search over documents, question answering, and health-check tools.
Based on the description, it can handle knowledge-base queries, document-based Q&A, and service health checks. Whether it supports more capabilities is not stated in the provided material; see the source repository.
The provided information only says that it connects to a multimodal RAG service, but it does not include installation steps, runtime requirements, or API key details. See the source repository for specific prerequisites.
Use RAG tools for knowledge retrieval, document management, and search visualization.
Search and add traceable RAG knowledge for each project workspace.
Turn unstructured documents into a searchable knowledge base for AI agents.
Index documents and retrieve knowledge via keyword and vector search over MCP.
Use authenticated MCP tools for graph-augmented and hybrid RAG retrieval.
Expose modular retrieval and reasoning tools to AI assistants through MCP.