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ai-coord-mcp

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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

    A4/5.0

    Scored across 16 tools

    Disambiguation5/5

    Each tool targets a distinct resource/action: task CRUD, claiming, lifecycle transitions (complete/cancel/reopen/approve/revision), messaging, progress, and waiting. Even similar tools like list_tasks/search_tasks and claim_task/claim_next_task are clearly differentiated by purpose and described behavior.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern in snake_case (create_task, get_task, claim_task, update_progress, wait_for_task). Verb choices are semantically aligned with actions (list/get/search for reads; claim/complete/cancel for state changes), making the set predictable and easy to navigate.

    Tool Count4/5

    At 16 tools, the set sits at the upper boundary of typical scope, but every tool serves a distinct purpose in the coordination workflow. The slightly elevated count is justified by the inclusion of specialized tools like claim_next_task and wait_for_task that optimize common operations.

    Completeness4/5

    The surface covers the full task lifecycle (create, read, claim, complete, cancel, reopen, approve, revision) plus messaging and progress tracking. Minor gaps exist: there's no tool to edit task metadata (e.g., title, description, assignee) after creation, but this can be worked around via cancel/recreate.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources