Intervals.icu MCP Server
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Alternatives to Intervals.icu MCP Server
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- FlicenseAqualityDmaintenanceEnables 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-
- AlicenseAqualityDmaintenanceEnables interaction with Intervals.icu data, including activities, wellness, and calendar events, via natural language.1045 npm1MIT
- AlicenseAqualityBmaintenanceEnables Claude and ChatGPT to access and manage Intervals.icu data, including activities, events, wellness metrics, power curves, and custom items through natural language.26GPL 3.0
- AlicenseAqualityCmaintenanceEnables Claude and ChatGPT to retrieve and manage activities, intervals, events, wellness data, power curves, and custom items through the Intervals.icu API.21GPL 3.0
- AlicenseNot gradedqualityCmaintenanceEnables Claude AI to access and manage intervals.icu training data, including workouts, wellness, and fitness trends, through natural language conversation.MIT
- AlicenseNot gradedqualityCmaintenanceEnables Claude to retrieve recent activities and wellness data and to create, update, or delete structured workouts that sync through intervals.icu to Garmin Connect and the user's watch.MIT
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: get_activities retrieves individual activity records, get_grouped_activities provides aggregated activity summaries, get_wellness fetches individual wellness metrics, and get_grouped_wellness offers aggregated wellness trends. The descriptions explicitly differentiate between individual data retrieval and grouped analysis for both domains.
All tools follow a perfectly consistent verb_noun pattern with clear prefixes: get_activities, get_grouped_activities, get_wellness, get_grouped_wellness. The naming convention is uniform throughout, using snake_case consistently and maintaining parallel structure between activity and wellness domains.
Four tools is reasonable for a fitness/wellness data server, covering the core domains of activities and wellness with both individual and grouped variants. However, the count feels slightly minimal - there might be room for additional operations like creating or updating records, though the current set adequately serves basic retrieval and analysis needs.
The toolset provides excellent read/analysis coverage for activities and wellness data, but lacks any write operations (create, update, delete). For a fitness tracking platform where users might want to log new activities or update wellness metrics, this represents a notable gap. The surface is complete for retrieval and analysis but incomplete for full CRUD lifecycle management.