Retrieval Lens

A black-box flight recorder for RAG retrieval inside MCP agents.

retrieval-lens is an MCP server that logs every retrieval step your RAG agent makes — what chunks were retrieved, their scores, sources, and rankings — so you can audit, replay, and diff retrieval runs after the fact.

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The Problem

When a RAG agent gives a wrong answer, you need to know: did retrieval fail, or did generation fail? Right now there's no easy way to answer that. Your observability tool shows you the LLM call. It doesn't show you which chunks the model saw before it answered, what scores they had, or how retrieval changed between yesterday and today.

retrieval-lens fixes that. Every retrieval run is logged. Nothing is hidden.

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Demo

When your RAG agent gives a wrong answer, ask retrieval-lens what it saw:

await mcp.call("retrieval_diff", {
  run_id_a: "support-bot-before-embedding-refresh",
  run_id_b: "support-bot-after-embedding-refresh",
  match_by: "source"
});

See docs/demo-diff.png for real output from Claude Code.

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MCP Tools

Tool	What it does
retrieval_observe	Log a retrieval run — query, chunks, scores, sources, rankings
retrieval_query	Replay what the model saw before a specific answer
retrieval_diff	Compare two retrieval runs — what changed, what score drifted
retrieval_stats	Aggregate score distributions, top sources, runs over time

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Quickstart

Run retrieval-lens directly with npx:

npx retrieval-lens

Add retrieval-lens to Claude Code with one command:

claude mcp add retrieval-lens npx retrieval-lens

Then call retrieval_observe after every retrieval step in your RAG pipeline:

await mcp.call("retrieval_observe", {
  run_id: crypto.randomUUID(),
  query: "what is the refund policy?",
  chunks: [
    { content: "Refunds are processed within 5 days...", score: 0.91, source: "policy.md", rank: 1 },
    { content: "Contact support for refund requests...", score: 0.74, source: "faq.md", rank: 2 }
  ],
  pipeline_tag: "support-bot"
});

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Adapters

LangChain

See examples/langchain-adapter.ts

LlamaIndex

See examples/llamaindex-adapter.ts

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Why not LangSmith / Langfuse?

Those are full observability platforms. retrieval-lens is surgical:

• Local-first — SQLite, zero signup, no data leaves your machine
• MCP-native — one config line, works in any MCP client
• Retrieval-only — focused on the layer where most RAG failures actually happen

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Status

🚧 Active development. Harness-first build using harness engineering principles.

• F05 — scaffold
• F01 — retrieval_observe
• F02 — retrieval_query
• F03 — retrieval_diff
• F04 — retrieval_stats

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License

MIT

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