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Use MCP to query knowledge hubs, search collections, and summarize documents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Modular RAG MCP Server" yet — see the docs or source repo.
Use MCP to query the knowledge hub for information about “RAG reranking” and return the most relevant results.
Returns relevant search results ranked by relevance.
List the available document collections and briefly explain each collection's purpose.
Outputs a list of available collections with brief descriptions.
Generate a summary for the specified document, highlighting the main points and key conclusions.
Produces a concise document summary.
Developers can use it to search across multiple collections with hybrid search and reranking. It helps quickly locate the most relevant technical documents.
Researchers can retrieve relevant entries from large document sets and then summarize them to decide what to read next.
Office workers can query the knowledge base and generate summaries to reduce manual searching and reading time.
It exposes knowledge hub queries, collection listing, and document summary tools via MCP, with hybrid search and reranking. You can use it to find relevant information and extract summaries from documents.
The provided material only says it is an MCP tool framework and does not include installation or runtime prerequisites. See the source repository for details.
It emphasizes modularity, observability, hybrid retrieval, and reranking, rather than only returning direct matches. It also provides a document summary tool.
Enable AI assistants to search, summarize, and use RAG knowledge bases via MCP.
Turn unstructured documents into a searchable knowledge base for AI agents.
Expose modular retrieval and reasoning tools to AI assistants through MCP.
Use RAG tools for knowledge retrieval, document management, and search visualization.
Retrieve and process docs with vector search to enrich AI responses.
Query private knowledge bases with modular RAG, hybrid retrieval, reranking, and observability.