RAGScore
Install RAGScore when you need reproducible QA sets and scored retrieval quality before shipping a RAG-powered product.
Overview
RAGScore is a MCP server for the Ship phase that generates QA datasets and evaluates RAG systems with failure diagnosis using your chosen LLM provider.
What is this MCP server?
- Generates QA datasets tailored to your corpus for repeatable RAG benchmarks
- Evaluates RAG systems with metrics and failure diagnosis across retrieval and generation
- Works with any LLM via OpenAI or Anthropic API keys
- Stdio MCP server (RAGScore v0.8.6) installable from PyPI as package ragscore
- Server version 0.8.6
- Transport: stdio
- Registry: PyPI identifier ragscore
What problem does it solve?
You cannot trust a RAG feature in production when you have no automated way to measure answer quality or pinpoint retrieval versus generation failures.
Who is it for?
Indie builders shipping RAG chatbots or internal copilots who already have a corpus and want MCP-driven eval without building a custom harness.
Skip if: Teams that do not use RAG, only need generic LLM chat with no retrieval, or refuse to provide LLM API keys for evaluation runs.
What do I get? / Deliverables
After registering RAGScore, your agent can run repeatable RAG evaluations and targeted failure reports so you fix the weakest link before users hit it.
- Generated QA datasets aligned to your RAG corpus
- RAG evaluation runs with failure diagnosis output
- Repeatable benchmark workflow invocable from your agent
Recommended MCP Servers
Journey fit
How it compares
RAG evaluation MCP server, not a vector database or embedding hosting service.
Common Questions / FAQ
Who is RAGScore for?
RAGScore is for solo builders and small teams building retrieval-augmented apps who need datasets and scored evals inside their coding agent.
When should I use RAGScore?
Use RAGScore when you are changing chunking, embeddings, or prompts and want failure diagnosis before ship or after user-reported bad answers.
How do I add RAGScore to my agent?
Register the stdio PyPI package ragscore in your MCP client config and set OPENAI_API_KEY and/or ANTHROPIC_API_KEY for your LLM provider.