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dorian-K

Alpha Progression MCP

by dorian-K

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Alternatives to Alpha Progression MCP

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    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.
      AGPL 3.0
    • A
      license
      B
      quality
      A
      maintenance
      Enables MCP clients to read account status, plans, and recent workouts, search official exercise names, and, when writes are enabled, apply confirmed preview-first plan changes and historical workout logs.
      22
      MIT
    • A
      license
      C
      quality
      A
      maintenance
      Enables reading training history and planning complete workouts on a personal calendar, including exercises with videos, sets, reps, per-set weights, supersets, notes, and block instructions. It also supports searching the exercise library, copying or moving sessions, and retrieving performance stats, personal records, and feedback.
      29
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    • F
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      D
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      Personal workout coach MCP server that logs exercises in natural language, tracks progress with SQLite, and provides coaching signals like estimated 1RM and volume trends.
      6
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    • A
      license
      Not graded
      quality
      C
      maintenance
      Strength-training app Claude can write to: reads real workout history (planned vs actual) and writes planned workouts back into the Tally iOS app. First-party hosted remote MCP server secured with OAuth 2.1 (PKCE + dynamic client registration).
      MIT

    TDQS

    A3.6/5.0

    Scored across 13 tools

    Disambiguation4/5

    Most tools target clearly distinct views: session summaries, single-session detail, plans, weekly load, progress trends, per-exercise history, exercise search, measurements. The main friction is the get_sessions/get_session pair and get_progress vs get_exercise_history, which require reading descriptions to separate (aggregate trend vs one exercise's rows), but the descriptions do make the boundary clear. get_document is a deliberate catch-all and is framed as a fallback, so it doesn't badly muddy selection.

    Naming Consistency4/5

    All names are snake_case verb_noun with a small, predictable verb set (get_/find_/set_/add_/swap_/remove_), and mutating plan tools share a clean add/swap/remove/set family. Minor deviations: singular/plural get_session vs get_sessions is the only noun-form irregularity, and find_exercises breaks the get_ prefix for a read operation. Overall readable and predictable.

    Tool Count5/5

    13 tools is squarely in the well-scoped range, split between 9 read/analysis tools and 4 plan-mutation tools. Each tool maps to a distinct question an agent would ask (how much did I train, is this lift stalling, what's on the plan) with no filler. The four plan-edit tools are arguably granular but each covers a real edit operation.

    Completeness3/5

    Read coverage of the training domain is strong (sessions, sets, plans, weekly volume, progress, history, measurements, arbitrary documents), and plan editing covers add/swap/remove/retarget. However the write surface is thin: there is no way to log a workout, record a body measurement, or create/rename a training plan, so an agent asked to capture new data hits a dead end. The preview/apply gating is good but doesn't fill those gaps.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues