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Query local Garmin health history and training data in natural language.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Garmin Health MCP Server" yet — see the docs or source repo.
Please query all Garmin workouts from this week and list the date, type, duration, and distance for each.
A weekly workout list organized by date with key metrics.
Summarize my sleep over the last 7 days, including daily duration and the overall trend.
A 7-day sleep summary with trend insights.
Query my training load, fitness scores, and records to see whether my recent status has changed.
An overview of training load, fitness scores, and record changes.
Use it to pull Garmin workouts, sleep, and load data into a weekly recap. It is useful for personal review and training summaries.
When you prefer not to send health data to the cloud, you can query the local SQLite database instead. The workflow emphasizes privacy and offline use.
If you need a specific workout, sleep entry, or gear record, you do not have to browse manually. Ask in natural language and get the result directly.
It provides read-only access to your local Garmin Connect history, letting you query workouts, sleep, training load, fitness scores, records, and gear in natural language. Data is first synced into a local SQLite database.
It emphasizes local and offline operation; queries use the local SQLite database. Whether internet is needed depends on the sync step that pulls data in.
No. This service is read-only and is only meant for querying local history data, not writing or modifying it.
Read Garmin Connect health and training data for queries and trend analysis.
Let AI access and analyze Garmin Connect fitness and health data.
Access and analyze Garmin fitness data to generate personal health insights.
Expose Garmin Connect health and training data to MCP-compatible AI assistants.
Access Garmin wellness metrics, summaries, and historical health trends through MCP tools.
Query live Garmin health, fitness, sleep, and activity trends in natural language.