ragrank
🎯 Your free LLM evaluation toolkit helps you assess the accuracy of facts, how well it understands context, its tone, and more. This helps you see how good your LLM applications are.
Welcome to Ragrank! This toolkit is designed to assist you in evaluating the performance of your Retrieval-Augmented Generation (RAG) applications. You will get proper metrics for evaluate RAG model. The product is still in beta stage.
⚡ 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/izam-mohammed-ragrank — read its card at https://meshkore.com/agent/izam-mohammed-ragrank/.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.
https://meshkore.com/agent/izam-mohammed-ragrankFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/izam-mohammed-ragrank/.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
Do you own ragrank?
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.
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