RAG-MCP Deep Dive

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  • RAG-MCP: Retrieve Tool Discovery, but Account for Routing Failure

    Intermediate Retrieval, memory, and production RAG
    RAG-MCP Deep Dive · Part 1 · Note · Mar 23, 2026 · Paper · 2025 · NLP

    A source-grounded reading of RAG-MCP's tool-routing pipeline, 11,100-tool stress test, MCPBench result, scale failure, and incomplete artifacts.

    Understand it in 90 seconds
    Problem
    placing every MCP tool schema in a prompt increases tokens, distractors, and wrong-tool selection.
    Core insight
    index MCP metadata, retrieve a small top-k schema set, then validate and invoke inside that set. Retrieval generates candidates; it does not authorize a decision.
    Strongest evidence
    on MCPBench web search, RAG-MCP reports 43.13% ground-truth MCP top-1 accuracy versus 18.20% for keyword pre-filter and 13.62% for all-schema prompting (Section 4.2; Table 1).
    Main boundary
    v1 does not fully expose retriever metadata, embedding/version, schema drift, permission, p95 latency, or real invocation success; top-1 routing is not task success.
    Read the full deep dive

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