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scholar-rag-mcp

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    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

    A3.7/5.0

    Scored across 11 tools

    Disambiguation5/5

    Each tool targets a distinct resource and action: KB-level, document-level, and chunk-level operations are cleanly separated. get_document versus get_document_text, and search_documents versus search_chunks, are differentiated by granularity and description.

    Naming Consistency5/5

    All tools follow a clear snake_case verb_noun pattern (get, add, remove, search, list, create, delete). The only slight abbreviation is list_kbs, but it does not break the pattern or create ambiguity.

    Tool Count5/5

    At 11 tools, the surface is well-scoped for a RAG/knowledge-base server: KB management, document management, retrieval, and job status. Each tool corresponds to a distinct operation needed for the workflow.

    Completeness5/5

    The set covers the full lifecycle: KB create/list/delete, document add/get/list/remove, text reading, document- and chunk-level search, and async job tracking. There are no obvious dead ends; asynchronous operations are paired with get_job for status and results.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources