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Garmin MCP

by lokranjanp
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Python
Remote

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"].

Related MCP server: garth-mcp-server

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.

This server cannot be installed

A
license - permissive license
-
quality - not tested
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
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