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hrishikesh-thakre

AI Workbench MCP

by hrishikesh-thakre
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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

    C2.9/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: analyzing runs, opening runs, quality gating, recording execution, selecting model, selecting policy pack, and validating runs. No overlap in functionality.

    Naming Consistency4/5

    All tools share the 'workbench_' prefix and mostly follow a verb_noun pattern (e.g., analyze_runs, open_run). Minor deviation with 'quality_gate' which is noun_verb, but overall consistent.

    Tool Count5/5

    With 7 tools, the set is well-scoped for an AI workbench run management domain. Not too few to lack utility, not too many to be unwieldy.

    Completeness3/5

    The tools cover core actions like opening, analyzing, recording, and validating runs, but lack lifecycle management tools such as listing, closing, or deleting runs. Some gaps exist for a complete workflow.

    Maintenance

    ActivityInactive
    ResponsivenessWithin a week

    Resources