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matrix-workouts-mcp

by bestimmaa

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    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI assistants to query and create workouts with structured intervals, manage calendars and events, analyze power PRs and CTL/ATL/TSB fitness trends, handle workout libraries, equipment and health metrics through natural language. Uses secure cookie-based authentication stored in the system keyring, so no API approval is required and any account can be set up in minutes.
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    • F
      license
      A
      quality
      D
      maintenance
      Enables AI assistants to access and analyze intervals.icu training data including activities, fitness metrics (CTL/ATL/TSB), wellness stats, and calendar events. Supports natural language querying of athletic performance for training insights and workout planning.
      6
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    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI assistants to query and analyze workouts, build structured intervals, manage calendars, events and libraries, and track fitness metrics such as CTL/ATL/TSB and power PRs through natural language. Uses secure cookie authentication stored in the system keyring, so no API approval or special account permissions are required.
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    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI assistants like Claude to query and manage endurance-training data through natural language, including workouts, structured intervals, calendars, events, workout libraries, strength sessions, health metrics, equipment, and coach tools. Provides fitness trend analysis (CTL/ATL/TSB), power and running PRs, FTP and zone updates, and annual training plan targets without requiring API approval, using keyring-stored cookie authentication.
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    • F
      license
      Not graded
      quality
      A
      maintenance
      Connects AI assistants to TrainingPeaks via secure cookie authentication (no API approval needed), letting users query and build workouts, schedule intervals on the calendar, analyze CTL/ATL/TSB fitness trends and power PRs, manage health metrics, events, equipment, and workout libraries through natural language. It also supports coach accounts with athlete groups and multi-week training plans.
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    TDQS

    A4/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool targets a distinct operation—listing, single-ride detail, raw samples, aggregates, comparison, export, and cache refresh—and the descriptions explicitly cross-reference the intended usage. No two tools overlap in function, and boundary cases like get_workout vs get_samples are clearly called out.

    Naming Consistency5/5

    All seven tools follow a consistent verb_noun snake_case pattern (list_, get_, summarize_, compare_, export_, refresh_). Singular and plural noun usage is logical and predictable, making tool selection straightforward.

    Tool Count5/5

    Seven tools is well-scoped for a workout-history MCP: the core read, analyze, and export paths are covered without redundancy. Each tool earns its place, and there is no sense of bloat or thinness.

    Completeness5/5

    The surface covers listing, detail, raw samples, aggregation, comparison, export, and cache refresh, so a user can go from finding a ride to analyzing or exporting it without dead ends. Create/update/delete are not needed because the server reads externally recorded rides and refresh_history handles new data.

    Maintenance

    ActivitySlowing
    ResponsivenessResponsive