Garmin MCP

MCP server that exposes Garmin Connect data (steps, sleep, stress, activities, etc.) via garth and fastMCP. Auth: set GARMIN_EMAIL and GARMIN_PASSWORD in env, or use a saved session at ~/.garth (or GARTH_SESSION_PATH).

MCP config

Use full path to uvx (e.g. which uvx). Example for Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json) or Cursor:

{
  "mcpServers": {
    "garmin": {
      "command": "/Users/YOUR_USER/.local/bin/uvx",
      "args": ["--refresh", "--from", "git+https://github.com/lokranjanp/garminmcp", "garmin-mcp"],
      "env": {
        "GARMIN_EMAIL": "your-garmin-email@example.com",
        "GARMIN_PASSWORD": "your-password",

        "SMTP_HOST": "smtp.gmail.com",
        "SMTP_PORT": "587",
        "SMTP_USER": "your-email@gmail.com",
        "SMTP_PASSWORD": "your-app-password",
        "SMTP_TO": "recipient@example.com",
        "OUTPUT_DIR": "/Users/user.mac/garmin_data/",

        "LLM_API_BASE": "http://localhost:1234/v1",
        "LLM_API_KEY": "lm-studio",
        "LLM_MODEL": "your-loaded-model-name"
      }
    }
  }
}

From PyPI (if published): "args": ["garmin-mcp"].

Commands

uv sync                    # install from uv.lock
uv run garmin-mcp          # run server locally
uv lock                    # refresh lock after changing pyproject.toml

Tools (17 base + 3 opt-in)

#	Tool	Description
	Utility
1	garmin_current_datetime	Current date/time (local & UTC, ISO, weekday)
	Auth
2	garmin_login	Log in and save session to disk
3	garmin_resume_session	Load saved session
	User
4	garmin_user_profile	Profile (display name, timezone, etc.)
5	garmin_user_settings	Settings (units, preferences)
	Metrics (daily/weekly series)
6	garmin_metric	Fetch daily or weekly stat series. metric: steps, sleep, stress, hydration, intensity_minutes, hrv. period: daily or weekly. count: number of periods.
	Detailed health data
7	garmin_data	Fetch detailed data (single day or list). data_type: sleep, hrv, weight, body_battery_events, body_battery_stress. Use day for single-day or end_date+days for a range.
	Activities
8	garmin_activities	List activities (start, limit)
9	garmin_activity_details	Full details for one activity by ID
10	garmin_activity_types	List all activity types (type IDs and keys)
	Biomarkers & summary
11	garmin_daily_summary	One-day wellness: RHR, HR, stress, steps, SpO2, respiration, body battery, calories
12	garmin_resting_heart_rate	Resting heart rate for a date range
13	garmin_summary_report	Comprehensive biomarker + activity report (daily/weekly/biweekly/monthly)
	Delivery
14	garmin_email_summary	Generate summary report and send via SMTP email (HTML + plain-text)
	Statistical analysis
15	garmin_stats	Run a stats operation on numeric data. operation: describe, percentiles, correlation, trend, compare, moving_average, outliers.
	Visualization (matplotlib)
16	garmin_viz	Render a chart. chart_type: line, bar, scatter, histogram, pie, heatmap, multi_line. Returns file path + base64 PNG.
	Raw API
17	garmin_connect_api	Call any Connect API path (GET/POST)
	LIDA / AI visualization (opt-in, requires LLM_API_KEY)
18	garmin_lida_visualize	AI-driven chart generation -- auto-picks the best chart type
19	garmin_lida_goals	Suggest N visualization goals for a dataset (EDA)
20	garmin_lida_explain	Explain a visualization's code in natural language

Dates: use YYYY-MM-DD; omit end date for "today" where supported.

SMTP env vars (for delivery tools)

Variable	Required	Description
SMTP_HOST	yes	SMTP server (e.g. smtp.gmail.com)
SMTP_PORT	no	Port, default 587 (STARTTLS)
SMTP_USER	yes	Login username / email
SMTP_PASSWORD	yes	Password or app-password
SMTP_FROM	no	Sender address (defaults to SMTP_USER)
SMTP_TO	no	Default recipient(s), comma-separated
SMTP_USE_SSL	no	"true" for implicit SSL (port 465); default uses STARTTLS

LLM env vars (for LIDA visualization tools -- opt-in)

The 3 LIDA tools (garmin_lida_*) are only registered when LLM_API_KEY is set. If omitted, the server loads with 17 tools instead of 20, reducing context overhead for the AI client.

Variable	Default	Description
LLM_PROVIDER	"openai"	LLM provider for LIDA
LLM_API_BASE	(none)	Custom endpoint -- set to http://localhost:1234/v1 for LM Studio
LLM_API_KEY	(none)	API key (use "lm-studio" for LM Studio). Setting this enables LIDA tools.
LLM_MODEL	"gpt-4o-mini"	Model name (for LM Studio: whatever model is loaded)

Visualization output is saved to output/viz/ (or OUTPUT_DIR env var) and also returned as base64 PNG in the JSON response.