OpenAthlete
Related Servers
Alternatives to OpenAthlete
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityAmaintenanceMCP server that mirrors your Garmin data into a personal database and exposes tools for health summaries, training load, muscle readiness, and race analysis, with optional chat-driven insights via stdio or HTTP.1MIT
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- AlicenseAqualityBmaintenanceAn MCP server that transforms Garmin data into compact, verdict-first coaching reports for LLMs, computing metrics like CTL/ATL/TSB and ACWR to answer training readiness questions.14Apache 2.0
- AlicenseAqualityBmaintenanceMCP server that connects AI agents to Intervals.icu, enabling access to training data such as activities, wellness metrics, calendar events, gear, power curves, and custom items. Includes a running coach AI template for automated post-session analysis.1916 npm1MIT
- FlicenseNot gradedqualityCmaintenanceMCP server for TrainingPeaks that reads workouts, fitness metrics (CTL/ATL/TSB), and health data, and creates/updates/deletes planned workouts. Enables AI-powered training plan management and automated adjustment based on performance, fatigue, and injury.-
- AlicenseBqualityAmaintenanceAn MCP server that gives LLMs access to Garmin Connect data, including training, recovery, sleep, stress, VO2 Max, and running summaries for personalized fitness advice.24337 PyPI1MIT
TDQS
Scored across 9 tools
The tools mostly target distinct resources and actions. The only overlap is get_overview, which summarizes training load/recovery and may trigger sync, versus the specialized get_training_load, get_recovery_status, and sync_now; descriptions make the summary-vs-detail boundary clear enough.
All names use lowercase snake_case and are predominantly verb_noun patterns such as get_*, query_*, read_*, write_*, and create_*. sync_now is the only non-noun object but remains clear and consistent.
Nine tools is a good fit for a focused athlete-assistant surface. Each tool covers a distinct workflow, and there is no obvious redundancy or missing low-level utility.
Core read, sync, memory, and workout-creation workflows are covered. Notable gaps remain around the workout lifecycle (no update/delete and no explicit list/get workout) and plan mutation, though these may be delegated to platform MCPs.