garmin-local-mcp

Local-first Garmin data warehouse with an analysis-grade MCP server. Sync once, analyze forever, even when the API is down.

Why another Garmin MCP?

Every existing Garmin MCP server follows the same design: a thin live wrapper around Garmin's rate-limited, unofficial API. Each question your AI assistant asks becomes one or more live API calls that return huge raw JSON blobs (a single raw sleep response runs around 230 KB). Multi-month questions like "how does my sleep correlate with training load?" are impractical, and when Garmin changes its auth (as it did in March 2026, breaking the whole ecosystem), those servers go completely dark, even for data they already fetched yesterday.

This project inverts the architecture:

• Sync once, analyze forever. Incremental sync into a local warehouse: immutable raw JSON snapshots plus a SQLite database, in a directory you own.
• Server-side analysis, compact responses. Trends, correlations, personal baselines, and anomaly detection are computed locally and returned as small columnar tables in a single tool call. Typical responses are under 2 KB, so nothing floods the model's context.
• Offline resilience. An API breakage pauses new syncs only. Every query over already-synced history keeps working.
• A zero-auth fallback. A standalone decoder for Garmin's undocumented wellness FIT messages (sleep score, HRV, skin temperature, sleep stages, naps) ingests manually exported bundles with no login at all. No other Garmin MCP ships this.
• Curated tools. 12 composable tools, not 110.

Quickstart

Requires Python 3.12+.

pip install garmin-local-mcp

Or run it without installing, via uv:

uvx garmin-local-mcp --help

1. Log in once (MFA supported; tokens persist locally, so future runs never ask for a password):

garmin-local-mcp login

2. Backfill your history. The sync is resumable, safe to interrupt, and throttled to be polite to Garmin's servers. A year of history is roughly 1,800 requests; for long backfills, start it and let it run (overnight works well). If it gets rate limited or interrupted, re-run the same command and it resumes where it left off.

garmin-local-mcp sync --from 2026-01-01

3. Register the MCP server with your client (see Client setup for Claude Desktop, Cursor, and other clients):

claude mcp add --scope user garmin -- garmin-local-mcp serve

4. Ask questions. Examples of what Claude can now answer from your local warehouse in one or two tool calls:

• "How does my sleep score correlate with next-day resting HR?"
• "What were my anomalous HRV days this quarter?"
• "Show weekly training load vs sleep for the last 3 months."

Client setup

The server speaks stdio, so any MCP client works. pip install garmin-local-mcp first (or use the uvx variants below, which need nothing installed beyond uv).

Claude Code

claude mcp add --scope user garmin -- garmin-local-mcp serve

Claude Desktop, one-click: download garmin-local-mcp-x.y.z.mcpb from the latest release, then in Claude Desktop open Settings > Extensions > Advanced settings, click "Install Extension…", and select the file. Requires uv on your PATH; the extension installs and runs the server from PyPI via uvx, so no manual Python setup is needed. If the install dialog warns about a missing Python >=3.12, you can ignore it: uv provisions its own interpreter.

Claude Desktop, manual (Settings, then Developer, then Edit Config; add to claude_desktop_config.json):

{
  "mcpServers": {
    "garmin": {
      "command": "garmin-local-mcp",
      "args": ["serve"]
    }
  }
}

Cursor (~/.cursor/mcp.json, or .cursor/mcp.json in a project):

{
  "mcpServers": {
    "garmin": {
      "command": "garmin-local-mcp",
      "args": ["serve"]
    }
  }
}

Any other stdio client / no local install (requires uv):

{
  "mcpServers": {
    "garmin": {
      "command": "uvx",
      "args": ["garmin-local-mcp", "serve"]
    }
  }
}

Note: login and the initial backfill sync are CLI steps (see Quickstart); the MCP server itself never prompts for credentials.

The 12 tools

Only sync and import_fit write anything, and only inside the data directory. The server never prompts: auth problems come back as structured errors with a hint pointing at the login CLI.

Available metric names include resting_hr, sleep_score, hrv, steps, stress_avg, body_battery_high, skin_temp_dev_c, vo2max, training_load, and about 25 more; any tool given an unknown name returns the full list.

Data layout and ownership

Everything lives in one directory you own (default ~/.garmin-mcp, override with the GARMIN_MCP_DATA_DIR environment variable or --data-dir):

~/.garmin-mcp/
├── config.toml                                  # optional settings
├── tokens/                                      # Garmin Connect session tokens
├── raw/daily/YYYY/YYYY-MM-DD/<endpoint>.json    # immutable raw API snapshots
├── raw/activities/<activity_id>.json            # one snapshot per activity
└── garmin.db                                    # SQLite warehouse

The raw JSON snapshots are the source of truth and are never overwritten. The SQLite database is a derived, rebuildable index: garmin-local-mcp reparse rebuilds it from the raw snapshots entirely offline, which is the universal escape hatch for schema evolution and parser fixes. Your data never leaves your machine.

Data quality note

Garmin watches report a provisional on-device resting heart rate that can diverge sharply from Garmin Connect's finalized value on nights with sparse sampling. A real observed case: the watch reported 69 bpm on-device while Garmin Connect later finalized the same night at 56 bpm.

This project handles that in two ways:

• The API sync stores Garmin Connect's finalized value.
• The FIT importer cross-checks the provisional on-device value against the overnight heart-rate floor. A resting HR sitting more than 10 bpm above the lowest overnight sample is a rate the watch never actually observed; it gets flagged (rhr_far_above_hr_floor) and withheld, leaving the field for the API to backfill rather than storing a misleading number.

Sparse sleep-stage logging is flagged the same way (sparse_sleep_stage_logging), and flags surface in get_day so the analysis layer knows which numbers to trust.

Offline / fallback runbook

If Garmin breaks the unofficial API again (it has before):

1. Everything analytical keeps working. All query, correlation, baseline, anomaly, and gap tools run on your already-synced local history. Only new syncs pause.
2. Keep ingesting without auth. Download a daily FIT bundle from the Garmin Connect website and import it locally (exact steps below). garmin-local-mcp import-fit <folder> decodes the bundle with zero authentication and fills the gap days. FIT-sourced rows never overwrite API-sourced rows (unless you pass --force).
3. Resume when the community catches up. Watch the python-garminconnect project for a fix, upgrade, and run garmin-local-mcp sync again. Thanks to resumable sync state, it picks up exactly where it stopped.

Downloading a wellness bundle, step by step

1. Sign in at connect.garmin.com in any browser.

2. Go directly to https://connect.garmin.com/app/settings/accountInformation (or click your avatar in the top-right corner, then Settings, then Account Information in the left sidebar).

3. Scroll to the bottom of the page, to the section titled Export Wellness Data ("Download your wellness FIT files from a specific day. This includes data such as steps, sleep, stress, HRV and more.").

4. Pick a date in the Date field and click Export. Your browser downloads a small zip for that one day, containing roughly 12 to 15 binary .fit files (*_WELLNESS.fit, *_SLEEP_DATA.fit, *_HRV_STATUS.fit, *_SKIN_TEMP.fit, *_METRICS.fit, and similar).

5. Unzip it into a folder and run:

   hljs language-wasm

   Copy

   garmin-local-mcp import-fit "path/to/unzipped/folder"
   

6. Repeat for each missing day (one bundle per date). The gaps tool or garmin-local-mcp status tells you which days need filling.

Two things worth knowing:

• Overnight sleep belongs to the wake date. To get last night's sleep, export yesterday's date if you slept into this morning, i.e. the date you woke up on.
• This per-day export is instant and separate from Garmin's full account export (the "Data Management" link on the same page), which is a bulk archive that can take days to arrive by email and is not what import-fit expects.

Configuration

Optional config.toml in the data directory:

Environment variables:

Development

python -m venv .venv
.venv/bin/pip install -e .[dev]     # Windows: .venv\Scripts\pip install -e .[dev]
pytest
ruff check .

The test suite runs fully offline against sanitized JSON fixtures and small FIT samples; CI never touches the live API.

Disclaimer

This project is not affiliated with, endorsed by, or supported by Garmin Ltd. It uses the community python-garminconnect library with your own credentials to access your own data. Garmin's APIs are unofficial and can change or break at any time; when that happens, your synced history remains fully usable and the FIT import path keeps working.

All data stays on your machine. Nothing phones home: no telemetry, no third-party services, no cloud. Treat your data directory like the personal health record it is, and never commit it to a repository.

License

MIT