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io.github.snoozelieb/coach-mcp

Adaptive AI training coach MCP server backed by Garmin Connect

Open source Open in the app JSON README (API)

About

Adaptive AI training coach MCP server backed by Garmin Connect

Details

Kind
MCP servers
Topic
No topic detected
Publisher
snoozelieb
Origin
official
Category
ferramentas
Transport
local
Version
1.0.0
Stars
1
Forks
1
Last push
2026-08-27T20:20:01Z
Repository state
ativo
Language
Python
License
MIT
Added
2026-08-29 04:01:25
Updated
2026-08-29 04:01:25
Origin id
io.github.snoozelieb/coach-mcp

README

# coach-mcp

<!-- mcp-name: io.github.snoozelieb/coach-mcp -->

[![CI](https://github.com/snoozelieb/coach-mcp/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/snoozelieb/coach-mcp/actions/workflows/ci.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

An opinionated AI training coach as an MCP server. It pulls your real data from
Garmin Connect and **prescribes with authority** — science-based load management
(ACWR), **code-enforced injury gates** (the server rejects plans that violate an
active injury restriction, no matter what the LLM says), and **persistent
coaching memory** so decisions, rationale, and your adaptation patterns survive
between conversations. It will tell you "no" when your enthusiasm exceeds your
capacity.

All health data and credentials stay on your machine — see
[Security & Privacy](#security--privacy).

## Quickstart

You need Python 3.12+, a free Garmin Connect account, and an MCP client
(Claude Code, Claude Desktop, or Cursor).

### Option A: uvx (recommended)

No install step — your MCP client runs the server on demand:

```bash
uvx garmin-coach-mcp
```

Jump to [Connect your MCP client](#connect-your-mcp-client) and use `uvx` as
the command.

### Option B: from source

```bash
git clone https://github.com/snoozelieb/coach-mcp.git
cd coach-mcp

python -m venv .venv
# Linux/macOS:
source .venv/bin/activate
# Windows:
.venv\Scripts\activate

pip install -r requirements.txt
cp .env.example .env   # then edit: GARMIN_EMAIL, GARMIN_PASSWORD
python server.py
```

## Connect your MCP client

The server needs two environment variables: `GARMIN_EMAIL` and
`GARMIN_PASSWORD`. Optional: `COACH_DATA_DIR` (where your coaching data lives)
and `ANTHROPIC_API_KEY` (only for the standalone `daily_loop.py --llm` script).
From a source checkout, a `.env` file works too.

### Claude Code

```bash
claude mcp add coach-mcp \
  --env GARMIN_EMAIL=you@example.com \
  --env GARMIN_PASSWORD=your_garmin_password \
  -- uvx garmin-coach-mcp
```

Or in `.mcp.json`:

```json
{
  "mcpServers": {
    "coach-mcp": {
      "command": "uvx",
      "args": ["garmin-coach-mcp"],
      "env": {
        "GARMIN_EMAIL": "you@example.com",
        "GARMIN_PASSWORD": "your_garmin_password",
        "COACH_DATA_DIR": "/path/to/your/coach-data"
      }
    }
  }
}
```

Running from source instead: `claude mcp add coach-mcp -- python /full/path/to/coach-mcp/server.py`

### Claude Desktop

In `claude_desktop_config.json` (Settings → Developer → Edit Config):

```json
{
  "mcpServers": {
    "coach-mcp": {
      "command": "uvx",
      "args": ["garmin-coach-mcp"],
      "env": {
        "GARMIN_EMAIL": "you@example.com",
        "GARMIN_PASSWORD": "your_garmin_password",
        "COACH_DATA_DIR": "/path/to/your/coach-data"
      }
    }
  }
}
```

### Cursor

In `.cursor/mcp.json` (project) or `~/.cursor/mcp.json` (global):

```json
{
  "mcpServers": {
    "coach-mcp": {
      "command": "uvx",
      "args": ["garmin-coach-mcp"],
      "env": {
        "GARMIN_EMAIL": "you@example.com",
        "GARMIN_PASSWORD": "your_garmin_password",
        "COACH_DATA_DIR": "/path/to/your/coach-data"
      }
    }
  }
}
```

If you installed with `pip install garmin-coach-mcp` instead of uvx, use
`"command": "garmin-coach-mcp"` with no args in any of the blocks above.

## First run

1. **Create your profile.** From a source checkout, run the interactive wizard:

   ```bash
   python scripts/setup_wizard.py
   ```

   It creates your athlete profile, training config, and empty plan/memory
   files in the data directory. Alternatively, create the two required files
   by hand and let the coach fill in the rest via conversation:

   ```bash
   echo '{"personal":{"name":null},"injury_history":[],"life_constraints":{}}' > data/athlete.json
   echo '{"events":[],"current_block":{"phase":"base"}}' > data/training_config.json
   ```

2. **Pull your Garmin baseline.** In your MCP client, say:

   > "Run refresh_athlete_baseline and set up my training."

   The coach pulls your name, weight, age, HR data, and training capacity from
   Garmin, then starts the onboarding conversation — goals, constraints,
   injury history, race calendar.

3. **Garmin MFA / expired session.** Garmin logins are token-cached. If tools
   start returning `AUTH_REQUIRED`, recover with:

   ```bash
   python scripts/garmin_login.py
   ```

   It does a fresh credential login, prompts for the MFA code if Garmin asks,
   and saves new tokens. Restart the MCP server afterwards.

## How it works

1. **Snapshot first** — every coaching conversation starts from
   `get_coaching_snapshot()`: current time context, 7-day week grid (rest days
   explicit), fitness metrics, plan adherence, open anomalies, injuries, sleep
   gate.
2. **Load hierarchy before prescribing** — overall ACWR (injury gate, 0.8–1.3
   sweet spot), then sport-specific ACWR (spike detection), then
   sport-specific CTL (race readiness).
3. **Hard gates are code, not vibes** — `update_weekly_plan` and
   `push_plan_to_garmin` reject sessions that violate an active injury's
   restricted activities, and every non-rest session must carry a `purpose` or
   the save is refused.
4. **Curiosity with memory** — planned-vs-actual anomalies (missed session,
   type mismatch, activity on a rest day) register once with a lifecycle
   (open → asked → resolved); the coach asks you what happened instead of
   silently assuming.
5. **Everything persists** — decisions, approvals, adaptation patterns, and
   season lifecycle (race debriefs, phase transitions) live in local JSON and
   carry across sessions.

## MCP surface

49 tools — you don't call them directly; the coach uses them during
conversation:

| Category | Tools |
|----------|-------|
| **Coaching core** | `get_coaching_snapshot` (canonical, sectioned), `get_compliance_report`, `get_coaching_score` |
| **Planning** | `get_weekly_plan`, `update_weekly_plan`, `push_plan_to_garmin`, `get_week_constraints`, `get_weekly_prescription`, `get_periodization_status`, `update_phase` |
| **Garmin data** | `query_metrics` (kind=fitness/intensity/daily/readiness/personal_records), `get_activities_range` |
| **Athlete** | `get_athlete`, `update_athlete`, `set_ftp`, `set_threshold_pace`, `analyze_ftp_test`, `refresh_athlete_baseline`, `refresh_fitness_history`, `get_onboarding_guide` |
| **Methodology** | `get_methodology`, `update_methodology` |
| **Races** | `races` (action=list/add/update/research), `remove_race` |
| **Strength** | `sync_strength_session`, `get_strength_baseline`, `approve_progression`, `set_exercise_preference`, `generate_strength_workout`, `add_exercise` |
| **Injuries** | `diagnose_injury`, `research_injury`, `update_injury_status` |
| **Research** | `research_exercise`, `list_exercises`, `research_sport` |
| **Memory** | `log_coaching_decision`, `get_active_decisions`, `update_decision_status`, `record_athlete_response`, `get_response_patterns`, `resolve_anomaly` |
| **Approvals** | `propose_coaching_action`, `list_pending_approvals`, `approve_proposal`, `reject_proposal` |
| **Interactive** | `generate_smart_brief`, `interactive_check_in` |

Every tool carries MCP annotations (read-only / destructive / idempotent /
open-world), enforced by tests.

**5 prompts**: `weekly_planning`, `morning_brief`, `injury_assessment`,
`week_review`, `onboarding`.

**6 resources**: `coach://athlete/profile`, `coach://plan/current`,
`coach://config/training`, `coach://coaching/decisions`, `coach://context/now`,
`coach://coaching/doctrine` (the long-form coaching doctrine).

## Security & Privacy

Everything stays on your machine:

- **Credentials**: `GARMIN_EMAIL`/`GARMIN_PASSWORD` live in your MCP client
  config or a local `.env`. Garmin OAuth tokens are cached in a local token
  store (`.garth/` in a source checkout, a per-user `garmin-tokens` directory
  for installed copies; override with `COACH_TOKEN_DIR`).
- **Health data**: all coaching data (profile, plans, fitness history, sleep,
  coaching memory) is local JSON in your data directory. There is no backend,
  no telemetry, no analytics.
- **What leaves your machine**: requests to Garmin's own API (your
  credentials/tokens, sent only to Garmin); whatever your MCP client sends to
  its LLM as part of the conversation; optional public web-page fetches when
  the coach researches a race, injury, or exercise; and, only if you run
  `daily_loop.py --llm`, one request to the Anthropic API.
- **Single athlete per data directory** by design. For multiple athletes, run
  separate server instances with separate `COACH_DATA_DIR`s.

See [SECURITY.md](SECURITY.md) for details and how to report issues.

### Data directory

Resolution order: `COACH_DATA_DIR` env var → `data/` in a source checkout → a
per-user data directory (created on first run for installed packages). The
only file shipped with the package is `methodology.json` (safety rules, race
templates, personas); everything personal is created locally and never
committed.

## Advanced

```bash
# HTTP transport (streamable-http) instead of stdio
COACH_TRANSPORT=streamable-http FASTMCP_PORT=8000 garmin-coach-mcp

# Code Mode (search/execute meta-tools instead of 49 individual tools)
pip install fastmcp[code-mode]
COACH_CODE_MODE=1 garmin-coach-mcp

# Standalone morning audit
python scripts/daily_loop.py          # template-based brief
python scripts/daily_loop.py --llm    # LLM brief (needs ANTHROPIC_API_KEY)

# Tests (1,333 tests; clean checkouts use committed sanitized fixtures)
pip install -r requirements-dev.txt
python -m pytest -q
```

## Architecture

`server.py` registers tools from the `coach/` package (11 tool modules, pure
parsers, a typed pydantic storage layer, CTL/ATL/ACWR fitness math, a Garmin
client with token-first auth, and a workout builder that pushes structured
workouts to your watch). The project went through a five-phase modernization —
auth rebuild, schema layer, hard gates, sectioned snapshot, packaging — whose
full history and rationale live in
[docs/UPGRADE_ROADMAP.md](docs/UPGRADE_ROADMAP.md). Development conventions
are in [CLAUDE.md](CLAUDE.md).

## License

[MIT](LICENSE)

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