Model Context Protocol server for Garmin Connect API integration
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"garmin-connect-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# Garmin Connect MCP Server  A Model Context Protocol (MCP) server for Garmin Connect integration. Access your activities, health data, training metrics, and more through Claude and other LLMs. [](https://www.python.org/downloads/) [](https://pypi.org/project/garmin-connect-mcp/) [](https://github.com/eddmann/garmin-connect-mcp/pkgs/container/garmin-connect-mcp) ## Overview This MCP server provides 22 tools to interact with your Garmin Connect account, organized into 8 categories: - Activities (3 tools) - Query activities and view detailed metrics - Analysis (2 tools) - Compare activities and find similar workouts - Health & Wellness (4 tools) - Access health metrics, sleep, heart rate, and activity data - Training (3 tools) - Analyze training periods and performance trends - User Profile (1 tool) - Access profile, statistics, and personal records - Challenges & Goals (2 tools) - Track goals, PRs, badges, and challenges - Devices & Gear (2 tools) - Manage devices and equipment - Weight Management (2 tools) - Track weight data - Other (3 tools) - Workouts, manual data entry, women's health tracking Additionally, the server provides: - 3 MCP Resources - Athlete profile, training readiness, and daily health for ongoing context - 6 MCP Prompts - Templates for common queries (training analysis, sleep quality, readiness checks, activity analysis, run comparison, health summary) ## Prerequisites - [uv](https://github.com/astral-sh/uv) (the package requires Python 3.12+, which uv can manage), OR - Docker ## Installation & Setup ### How Authentication Works 1. Credential Authentication - Run the setup command to save credentials 2. MFA Support - If MFA is enabled, the setup command prompts for your code 3. Token Storage - OAuth …
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.