intervals.mcp
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- FlicenseAqualityDmaintenanceEnables AI assistants to interact with Intervals.icu fitness tracking and wellness data, allowing users to fetch, filter, and group activities or health metrics. It provides structured summaries of workouts and physical well-being through natural language queries.4-
- AlicenseAqualityDmaintenanceEnables interaction with Intervals.icu data, including activities, wellness, and calendar events, via natural language.1018 npm1MIT
- AlicenseNot gradedqualityCmaintenanceEnables Claude and ChatGPT to access and manage Intervals.icu data, including activities, events, wellness metrics, power curves, and custom items through natural language.GPL 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
- AlicenseAqualityBmaintenanceProvides AI assistants with read-only access to an athlete's Intervals.icu training data, including activities, wellness metrics, zones, and planned events, for use with MCP clients like ChatGPT and Claude.15MIT
- FlicenseAqualityBmaintenanceEnables chat to query cycling training data from Intervals, including athlete profiles, activities, wellness, and calendar events, via read-only tools.5-
TDQS
Scored across 6 tools
Each tool targets a distinct domain: activity listing vs. detailed retrieval, athlete profile, calendar events, fitness metrics (CTL/ATL/TSB), and wellness data. No overlap in purpose; the distinction between planned events and completed activities is clear.
Perfectly consistent verb_noun pattern using 'get_' prefix throughout. All use snake_case. The '_detail' suffix appropriately distinguishes single-resource retrieval from list operations without breaking the convention.
Six tools is ideal for this focused read-only integration covering the core intervals.icu data surfaces (activities, athlete, calendar, fitness, wellness). Each tool earns its place; neither bloated nor sparse.
Strong coverage of read operations for athlete analytics including list/detail views and time-series data. Minor gaps exist: no power curve retrieval, segment data, or write operations (create/update activities/events), but core analysis workflows are fully supported.