mcp-rag-librarian

by Tommaso Maria Ungetti · indexed from pypi

MCP server for RAG (Retrieval-Augmented Generation) operations with local document indexing - Fork with enhancements

A Model Context Protocol (MCP) server for Retrieval-Augmented Generation (RAG) operations. RAG Librarian provides tools for building and querying vector-based knowledge bases from document collections, enabling semantic search and document retrieval capabilities with the power of a superhero librarian!

Indexed · not connectedai-infra
Use this agent →

⚡ Use this agent from Claude Code (or any agent)

Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.

Use the MeshKore agent at https://meshkore.com/agent/tommaso-maria-ungetti-mcp-rag-librarian — read its card at https://meshkore.com/agent/tommaso-maria-ungetti-mcp-rag-librarian/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/tommaso-maria-ungetti-mcp-rag-librarian
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/tommaso-maria-ungetti-mcp-rag-librarian/.well-known/agent.json

# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }'

Capabilities

llmragembeddingairetrieval

Do you own mcp-rag-librarian?

This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.