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    • A
      license
      Not graded
      quality
      D
      maintenance
      A comprehensive AI-powered fitness tracking application that enables AI tools to interact intelligently with user fitness data, providing personalized workout plans, nutrition tracking, and progress analysis through natural language.
      15
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      A universal fitness intelligence layer for AI assistants like Claude, ChatGPT, and Copilot, enabling user profiles, workout/diet plans, calendar scheduling, and gamification via MCP and REST APIs.
      7,375 npm
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI assistants to plan and track McDonald's visits by locating stores, estimating commute calories, recommending nutrition-targeted menu combinations, monitoring orders, running spaced-repetition vocabulary practice during waits, and recording progress in a local dashboard.
      5
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI assistants and native clients to manage training plans, workout history, reusable recipes, meal records, and media through validated, versioned MCP and REST operations. It stores and validates structured fitness and nutrition data, including nutrition calculation, food catalog lookup, and Apple Health export status.
      MIT

    TDQS

    B3.4/5.0

    Scored across 16 tools

    Disambiguation4/5

    Each tool targets a distinct resource or action: profile get/update, facility/session/meal discovery, plan lifecycle, and adherence logging/summary. Minor overlap exists between plan_validate and plan_save (save re-validates) and between profile_get and readiness_questions, but the descriptions clarify the boundaries.

    Naming Consistency3/5

    Most names follow a verb_noun pattern (find_facilities, estimate_energy, plan_save), but several are noun phrases (readiness_questions, wearable_summary, adherence_summary), one is a single verb (geocode), and food_lookup reverses the verb/object order. The snake_case convention is consistent, but the semantic pattern is mixed.

    Tool Count4/5

    16 tools is slightly above the ideal 3-15 range, but the domain spans profile management, discovery, nutrition, wearables, plans, and adherence, and each tool has a distinct purpose. No tools feel redundant.

    Completeness3/5

    Core workflows are covered: profile get/update, plan validate/save/get, adherence log/summary, and location/nutrition discovery. Obvious gaps include no plan delete/history, no retrieval of raw adherence entries, and no booking action for discoverable sessions or facilities.

    Maintenance

    ActivitySlowing
    ResponsivenessUnresponsive