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mcp-research-pipeline

by rubayatkhan
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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

    A3.6/5.0

    Scored across 15 tools

    Disambiguation4/5

    Tools are mostly distinct by resource and action, with clear separation between YouTube retrieval and NotebookLM management. The main ambiguity is between get_channel_latest and get_channel_videos, though descriptions clarify the convenience vs. paginated distinction. research_topic overlaps with the individual steps but is clearly positioned as an orchestrator.

    Naming Consistency4/5

    All tool names use snake_case and generally follow a verb_noun pattern, which keeps the set predictable. Minor inconsistencies like get_channel_latest omitting a noun and mixing list_/get_ for similar operations prevent a perfect score.

    Tool Count4/5

    At 15 tools, the server sits at the upper bound of a well-scoped set, but the count is justified by covering two integrated domains: YouTube retrieval and NotebookLM management. research_topic and get_channel_latest add some redundancy but still serve useful convenience purposes.

    Completeness4/5

    The core research workflow is covered end-to-end: search YouTube, create a notebook, add sources, ask questions, and generate/download artifacts. Missing delete/update operations for notebooks, sources, and artifacts are notable gaps but not critical for the stated pipeline purpose.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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