About coach-mcp
coach-mcp is a Garmin Connect-backed training coach for athletes using MCP clients. It combines fitness-load-based planning, code-enforced injury restrictions, and persistent local coaching history.
Key features
- 01Garmin fitness and activity data
- 02ACWR-based load management
- 03Injury-gated plans and uploads
- 04Persistent coaching memory
- 05Training plans and Garmin workout uploads
Use cases
- 01Review recent training and readiness
- 02Create plans grounded in training history
- 03Prevent sessions that conflict with injuries
MCP support
An MCP server exposing coaching tools, prompts, and resources to clients. It can retrieve Garmin data, manage training plans, and push structured workouts to Garmin.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx garmin-coach-mcpGARMIN_EMAILGarmin Connect account emailGARMIN_PASSWORDSecretGarmin Connect account passwordCOACH_DATA_DIRDirectory for athlete data files (defaults to the git-checkout data/ dir when run from a checkout, otherwise a per-user data directory)Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEcoach-mcp
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.
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:
uvx garmin-coach-mcp
Jump to Connect your MCP client and use uvx as
the command.
Option B: from source
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);
COACH_TOKEN_DIR, COACH_TRANSPORT and COACH_CODE_MODE are covered under
Security & Privacy and Advanced.
From a source checkout, a .env file works too.
To run the server or the helpers from a terminal, set the two variables in that shell first. PowerShell and bash quote differently:
$env:GARMIN_EMAIL = "[email protected]"
$env:GARMIN_PASSWORD = "your-password"
export [email protected] GARMIN_PASSWORD=your-password
Claude Code
claude mcp add coach-mcp \
--env [email protected] \
--env GARMIN_PASSWORD=your_garmin_password \
-- uvx garmin-coach-mcp
Or in .mcp.json (Claude Code expands ${VAR} from your shell environment,
so a committed file holds no secret):
{
"mcpServers": {
"coach-mcp": {
"command": "uvx",
"args": ["garmin-coach-mcp"],
"env": {
"GARMIN_EMAIL": "[email protected]",
"GARMIN_PASSWORD": "${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):
{
"mcpServers": {
"coach-mcp": {
"command": "uvx",
"args": ["garmin-coach-mcp"],
"env": {
"GARMIN_EMAIL": "[email protected]",
"GARMIN_PASSWORD": "your_garmin_password",
"COACH_DATA_DIR": "/path/to/your/coach-data"
}
}
}
}
Cursor
In .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):
{
"mcpServers": {
"coach-mcp": {
"command": "uvx",
"args": ["garmin-coach-mcp"],
"env": {
"GARMIN_EMAIL": "[email protected]",
"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
-
Create your profile. Run the interactive wizard (installed copies have the console script; a source checkout can run the script):
garmin-coach-setup # pip / uvx install python scripts/setup_wizard.py # source checkoutUntil a profile exists the server runs in setup mode: plan, program and push writes answer
setup_requiredand name the data directory they looked in.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:garmin-coach-login # pip / uvx install python scripts/garmin_login.py # source checkoutIt 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
- 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. - 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).
- Hard gates are code, not vibes —
update_weekly_planandpush_plan_to_garminreject sessions that violate an active injury's restricted activities, and every non-rest session must carry apurposeor the save is refused. Multi-week programs (training_program) are rejected if their starting capacity contradicts your actual history. - Grounded in what you've actually done — prescriptions are anchored to
your demonstrated per-sport capacity from recent sessions, not template
numbers; a weekly plan that outruns that history comes back with
grounding_warnings. - 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.
- Everything persists — decisions, approvals, adaptation patterns, and season lifecycle (race debriefs, phase transitions) live in local JSON and carry across sessions.
MCP surface
51 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, training_program (action=get/set; multi-week programs, capacity-anchored and injury-gated), get_week_constraints, get_weekly_prescription, get_periodization_status, update_phase |
| Garmin data | query_metrics (kind=fitness/sport_history/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, backfill_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.
6 prompts: weekly_planning, morning_brief, injury_assessment,
week_review, onboarding, race_program.
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_PASSWORDlive 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-usergarmin-tokensdirectory for installed copies; override withCOACH_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. Nothing else: the FastMCP startup banner that would check PyPI for updates is disabled.
- Single athlete per data directory by design. For multiple athletes, run
separate server instances with separate
COACH_DATA_DIRs.
See 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
# 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 50 individual tools)
pip install fastmcp[code-mode]
COACH_CODE_MODE=1 garmin-coach-mcp
# Standalone morning audit
python scripts/daily_loop.py # ingest + audit yesterday + template brief (exit 1 if unverified)
# Tests (1,546 tests; clean checkouts use committed sanitized fixtures)
pip install -r requirements-dev.txt
python -m pytest -q
Architecture
coach/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.
Acknowledgements
All Garmin Connect access — login, MFA, token handling, activity and health data, and workout upload — runs on python-garminconnect by Ron Klinkien (@cyberjunky) and its contributors (MIT). coach-mcp wouldn't exist without it.
License
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.