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TuringMind MCP Server

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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

    A3.5/5.0

    Scored across 34 tools

    Disambiguation4/5

    Most tools have clearly distinct names and descriptions, but some overlap exists among state-querying tools like get_decision_queue, get_execution_state, get_ready_nodes, and get_spec_status. Descriptions help disambiguate, but the high number of tools increases potential confusion.

    Naming Consistency5/5

    All tools follow the consistent turingmind_verb_noun pattern (e.g., list_spec_nodes, apply_edit, classify_failure). Verbs like get, list, create, update, delete are used systematically, making the set predictable and easy to navigate.

    Tool Count2/5

    With 34 tools, the set exceeds the recommended range of 15-25 for high coherence. While each tool serves a distinct purpose in the complex TuringMind ecosystem, the sheer number overwhelms typical agent memory and selection, making it hard to browse efficiently.

    Completeness4/5

    The tool surface covers a full lifecycle for constraint-driven development: spec creation, update, promotion, verification, failure classification, repair, and auditing. Minor gaps exist (e.g., no explicit node deletion tool, only update), but the core workflows are well-supported.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources