Garmin Training MCP

A small, read-only MCP server for discussing your Garmin activities and training with ChatGPT, Claude, or another MCP-compatible assistant. Python 3.12+, FastMCP, and garminconnect.

This is an independent project using Garmin Connect's unofficial API. It is not affiliated with Garmin. Endpoint availability and login can change without notice. One server process serves one Garmin account.

Tools

Dates are explicit YYYY-MM-DD dates in your Garmin account's local calendar. The server preserves Garmin's raw fields and units. A missing value remains missing; it is never turned into zero. Training estimates depend on your device. Responses include their original fetch timestamp and are cached in memory for five minutes (at most 128 entries). Calls are serialized to avoid overlapping access to the Garmin session. There are no upload, edit or delete tools.

Related MCP server: Garmin Workouts MCP Server

Install and sign in

Install uv, then run:

git clone https://github.com/srichards2103/garmin-mcp.git
cd garmin-mcp
uv sync --locked
uv run garmin-mcp login

Enter your Garmin email, password and MFA code in your local terminal. The password and MFA prompts are hidden. Do not send credentials to an assistant or put them in GitHub. Sessions are stored at ~/.garmin-mcp, outside the repo. Set GARMIN_TOKEN_DIR to change that location; use the same setting for login and the server. On POSIX systems the directory is mode 0700 and files are 0600. Protect the directory with appropriate ACLs on Windows.

If login is rate limited, wait before retrying. If a session expires, run the login command again. Login and token refresh contact Garmin; the MCP tools do not alter your Garmin activities or settings.

Local MCP clients

uv run garmin-mcp serve

This uses stdio. Configure a stdio-capable MCP client with an absolute repository path, for example:

{
  "mcpServers": {
    "garmin": {
      "command": "uv",
      "args": ["--directory", "/absolute/path/to/garmin-mcp", "run", "garmin-mcp", "serve"]
    }
  }
}

The uv executable must be on that client's PATH; use its absolute executable path if necessary. Authenticate as the same OS user running the MCP process.

Connect to ChatGPT

Publishing this source on GitHub does not deploy a server or connect an account. For personal use, the initial connection route is OpenAI's Secure MCP Tunnel, which can forward requests to this private stdio server.

1. Check access to Platform tunnel settings and ChatGPT developer mode. Availability depends on account/workspace policy.

2. Follow the official tunnel guide to create a tunnel, associate it with your intended ChatGPT workspace, install tunnel-client, and configure its runtime credential locally.

3. Configure a stdio tunnel profile using this MCP command, with your absolute repository path: uv --directory /absolute/path/to/garmin-mcp run garmin-mcp serve.

4. Run the tunnel profile and its doctor check. In ChatGPT developer mode, add an MCP connection using Tunnel, then select the tunnel and review its tools.

5. Keep the server host and tunnel client running. Start a conversation with the connection enabled and ask for recent activities.

The official connection guide describes current UI steps and permissions. This project does not provision your tunnel or runtime credential. If your account cannot use tunnels, a future deployment needs a public HTTPS endpoint with authentication and authorization restricted to the account owner.

For local transport debugging there is also:

uv run garmin-mcp serve --transport http --port 8000

This binds only to 127.0.0.1, at /mcp. It has no application authentication. Do not expose it using a public port forward, proxy or public tunnel. The stdio route is preferable on shared machines; other local processes can reach a loopback HTTP listener. Public authenticated hosting is not implemented in v0.1.

Example questions

• “Show my most recent 20 activities and compare the runs.”

• “Get the splits and heart-rate zones for that run.”

• “Compare my sleep and HRV for 2026-09-01 through 2026-09-07.”

• “What does Garmin report for my training status on 2026-09-07?”

The assistant retrieves several daily records to compare a date range. Activity history is paginated; it must fetch enough pages before claiming completeness. Returned activity names and other text are data, not instructions.

Development

uv sync --locked
uv run ruff check .
uv run ruff format --check .
uv run pytest -q

Tests use synthetic responses; no Garmin credentials are needed in CI. The suite includes in-process MCP discovery and tool calls, SDK method availability, pagination, cache expiry and concurrency, missing metrics, validation, sanitized errors, and token permissions. A live Garmin login, live Garmin responses and an end-to-end ChatGPT connection still require manual verification.

Privacy and limitations

The code can be public; session tokens and health records must remain private. Garmin data returned by tools is shared with the connected AI client. Activity summaries can contain location information. The server does not persist health responses, but the client or hosting environment may retain them. Never attach real tokens or health records to public issues. See SECURITY.md.

References

• python-garminconnect

• FastMCP

• Garmin's official Health API

The official Garmin developer APIs are a separate integration route requiring program access. This project uses the personal-account community library.