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Adaptive AI training coach MCP server backed by Garmin Connect
Adaptive AI training coach MCP server backed by Garmin Connect
Valid MCP server (2 strong, 3 medium validity signals). 4 known CVEs in dependencies Package registry verified. Imported from the Official MCP Registry.
6 files analyzed · 5 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: GARMIN_EMAIL
Environment variable: GARMIN_PASSWORD
Environment variable: COACH_DATA_DIR
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-snoozelieb-coach-mcp": {
"env": {
"GARMIN_EMAIL": "your-garmin-email-here",
"COACH_DATA_DIR": "your-coach-data-dir-here",
"GARMIN_PASSWORD": "your-garmin-password-here"
},
"args": [
"garmin-coach-mcp"
],
"command": "uvx"
}
}
}From the project's GitHub README.
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.
You need Python 3.12+, a free Garmin Connect account, and an MCP client (Claude Code, Claude Desktop, or Cursor).
No install step — your MCP client runs the server on demand:
uvx garmin-coach-mcp
Jump to Connect your MCP client and use uvx as
the command.
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
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 mcp add coach-mcp \
--env GARMIN_EMAIL=you@example.com \
--env GARMIN_PASSWORD=your_garmin_password \
-- uvx garmin-coach-mcp
Or in .mcp.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
In claude_desktop_config.json (Settings → Developer → Edit Config):
{
"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"
}
}
}
}
In .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):
{
"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.
Create your profile. From a source checkout, run the interactive wizard:
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:
echo '{"personal":{"name":null},"injury_history":[],"life_constraints":{}}' > data/athlete.json
echo '{"events":[],"current_block":{"phase":"base"}}' > data/training_config.json
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.
Garmin MFA / expired session. Garmin logins are token-cached. If tools
start returning AUTH_REQUIRED, recover with:
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.
get_coaching_snapshot(): current time context, 7-day week grid (rest days
explicit), fitness metrics, plan adherence, open anomalies, injuries, sleep
gate.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.48 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).
Everything stays on your machine:
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/garmin_tokens.json).daily_loop.py --llm, one request to the Anthropic API.COACH_DATA_DIRs.See SECURITY.md for details and how to report issues.
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.
# HTTP / SSE transport instead of stdio
COACH_TRANSPORT=streamable-http FASTMCP_PORT=8000 garmin-coach-mcp
# Code Mode (search/execute meta-tools instead of 48 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 (1,260 tests; clean checkouts use committed sanitized fixtures)
pip install -r requirements-dev.txt
python -m pytest -q
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. Development conventions
are in CLAUDE.md.
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