garmin-connect-mcp
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Alternatives to garmin-connect-mcp
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Related Servers
- AlicenseBqualityCmaintenanceA read-only MCP server that lets AI agents query a user's own Garmin Connect data via Garmin's official Personal API. It exposes activities, daily wellness metrics, sleep and recovery, and body/profile data as tools for natural-language analysis.21Apache 2.0
- FlicenseAqualityCmaintenanceA local MCP server that exposes Garmin Connect activity, health, and training data as tools for Claude Desktop. It provides read-only access to metrics like activities, sleep, heart rate, and training readiness.25-
- FlicenseAqualityDmaintenanceMCP server that connects AI assistants to Garmin Connect health and fitness data, enabling natural language queries about workouts, sleep, heart rate, and more.29-
- AlicenseAqualityBmaintenanceThis server lets Claude, Cursor, or any MCP client read a user's own health and fitness data through 26 read-only tools for activities, sleep, HRV, Body Battery, stress, steps, heart rate, weight, nutrition, and women's health. It connects via the official Personal API and can be self-hosted for free on Vercel.2651 npmMIT
- AlicenseNot gradedqualityDmaintenanceThis MCP server exposes Garmin Connect health data—sleep, heart rate, HRV, stress, VO2max, and activities—to Claude through local tools, enabling natural language queries, data syncing, and statistical analysis like correlations and night-out detection.MIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server that gives Claude access to your Garmin Connect fitness and health data, including steps, sleep, activities, heart rate, and more.1MIT
TDQS
Scored across 97 tools
Each tool targets a distinct data type or operation, with clear boundaries between single-date vs. range variants and composite vs. granular endpoints. The 97 tools cover separate aspects (activities, health metrics, gear, badges, etc.) without significant overlap.
All tools follow a consistent verb_noun pattern in snake_case (e.g., get_heart_rate, set_blood_pressure, delete_activity, count_activities). Only a minor deviation with 'count' instead of 'get', but overall the pattern is uniform and predictable.
97 tools is large, but it reflects the comprehensive nature of Garmin Connect's health and fitness data. Each tool serves a specific purpose, and avoiding redundant endpoints would sacrifice usability. The domain justifies the count, though it borders on being heavy.
The set covers virtually all major aspects of the Garmin Connect API: activity CRUD, detailed metrics, health and wellness data (HRV, sleep, stress, etc.), gear management, badges, challenges, training plans, and user settings. There are no obvious gaps that would hinder core workflows.