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paintbrushv

plat-costmodel

by paintbrushv
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Python

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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

    B3.3/5.0

    Scored across 11 tools

    Disambiguation3/5

    Six tools revolve around estimation (estimate, estimate_scope, estimate_full_property, estimate_property_from_model, estimate_from_deal, prepare_renovation_program_tool), and the property-level pair (estimate_full_property vs estimate_property_from_model) is especially easy to confuse since one is merely the 'preferred' wrapper of the other. Descriptions do clarify levels (per-unit vs property vs deal vs pipeline), so an agent can usually recover, but boundaries are fuzzy.

    Naming Consistency4/5

    Nearly all tools follow a clean verb_noun snake_case pattern (check_renovation_roi, estimate_full_property, generate_scope_of_work, evaluate_contractor_bid, register_property, get_property_history). Minor deviations are the bare 'estimate' and the odd '_tool' suffix in prepare_renovation_program_tool, but these are isolated.

    Tool Count4/5

    11 tools is well within a healthy range for a cost-modeling server covering estimation, ROI, SOW, bid review, registration, and history. The only concern is slight redundancy in the estimation cluster, which nudges it below a perfect score.

    Completeness4/5

    The surface covers the core lifecycle: property registration, per-unit and property-level estimation, ROI gating, scope-of-work generation, contractor bid evaluation, deal-scenario projection, and history retrieval. Gaps are minor—no update/delete or list/get property operations, and no standalone exterior-only estimate—but agents can work around these.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources