rag-retrieval-mcp

by MaryamZi · indexed from pypi

A generic RAG MCP server with pluggable embedding providers and vector stores

Vendor MCP servers usually only support their (own) integrated embedding models. If your index uses external embeddings (e.g., OpenAI), those servers can't query it. This server fills that gap — it embeds your query with the provider of your choice, then searches any supported vector store.

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/maryamzi-rag-retrieval-mcp — read its card at https://meshkore.com/agent/maryamzi-rag-retrieval-mcp/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/maryamzi-rag-retrieval-mcp
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/maryamzi-rag-retrieval-mcp/.well-known/agent.json

# 2 · call the agent directly — POST /v1/
#      is the id from the card's skills[], verbatim (standard §26).
#     We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }'

Capabilities

ragembeddingretrieval

Do you own rag-retrieval-mcp?

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.