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grounded-rag-mcp

by chetan1521
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Python
Hybrid

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.2/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool targets a distinct operation: ingestion, search, grounded answering, collection listing, and retrieval evaluation. There is no overlap in purpose, and descriptions clearly delineate when to use each.

    Naming Consistency4/5

    Tool names follow a clear imperative, snake_case style. Most use verb_noun (ingest_documents, list_collections, evaluate_retrieval), though search and answer are bare verbs rather than verb_noun, creating a minor inconsistency.

    Tool Count5/5

    Five tools is well-scoped for a grounded RAG server: ingest, search, answer, list collections, and evaluate retrieval. Each tool covers a necessary part of the workflow without redundancy or bloat.

    Completeness4/5

    Core RAG workflows are covered end-to-end, including ingestion, retrieval, grounded answering, and quality evaluation. The main gap is lifecycle management: there is no way to delete or update documents or collections once ingested.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources