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psprakhar020

Azure Image Pipeline MCP

by psprakhar020
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Python
Remote

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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

    B3.4/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool targets a distinct resource+action: listing recent runs, fetching status of one run, gathering evidence artifacts, and queueing a pipeline. No two tools overlap in purpose, so misselection is unlikely.

    Naming Consistency5/5

    All four names follow a consistent verb_noun pattern (get_image_run_*, list_recent_image_runs, queue_image_pipeline) with a shared 'image' domain prefix. Predictable and readable throughout.

    Tool Count4/5

    Four tools is on the lean side but fits the narrow monitoring-and-queueing scope of an image pipeline. Each tool earns its place, though one or two more (e.g. logs/cancel) could round it out.

    Completeness4/5

    The surface covers the core lifecycle: discover runs, inspect status, gather release evidence, and trigger a run under a guarded confirmation. Minor gaps like canceling/retrying a run or fetching raw logs are workable around.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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