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Trust Engine MCP

Trust Engine MCP

by DinevDecor
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

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    TDQS

    A4.1/5.0

    Scored across 9 tools

    Disambiguation4/5

    Most tools have clearly separate roles: scan_for_models discovers models in code, check_my_models evaluates specific ids, model_retirements browses the catalogue, analyze_usage handles spend, and the pilot trio (prepare_pilot_tasks, request_pilot, pricing) splits drafting tasks, drafting email, and pricing. The retirement trio (scan/check/model_retirements) overlaps in subject matter and the descriptions spend a lot of words redirecting between them, which is the main source of possible misselection.

    Naming Consistency4/5

    All names are snake_case and readable, but the pattern mixes verb-led names (scan_for_models, analyze_usage, estimate_switch_savings, prepare_pilot_tasks, request_pilot) with bare noun names (benchmark_results, model_retirements, pricing). The deviation is minor and each noun name still maps to a clear resource.

    Tool Count5/5

    Nine tools is squarely in the well-scoped range, and each one maps to a distinct step in the retirement/cost workflow: discover, check, browse, analyse, benchmark, estimate, prepare, request, price. None looks like filler or a duplicate.

    Completeness4/5

    The surface covers the full advisory lifecycle for model retirement and cost optimisation, including a human-in-the-loop pilot handoff and pricing. Gaps are modest: savings estimates only work for benchmarked model pairs, usage exports only accept OpenAI/Anthropic, and there is no tool to set up the advertised 24/7 watch or re-check a model over time.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

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