Alpha Progression MCP
Related Servers
Alternatives to Alpha Progression MCP
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables 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
- AlicenseBqualityAmaintenanceEnables 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.22MIT
- AlicenseCqualityAmaintenanceEnables 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.29MIT
- AlicenseNot gradedqualityBmaintenanceEnables creating workout plans, tracking progress, suggesting exercises, and calculating training volume through natural language, compliant with MCP protocol.MIT
- FlicenseAqualityDmaintenancePersonal 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-
- AlicenseNot gradedqualityCmaintenanceStrength-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
Scored across 13 tools
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.
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.
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.
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.