Coach Mcp

io.github.snoozelieb由 GitHub 账号 snoozelieb 发布v1.4.0更新于 Nov 7, 2026

Adaptive AI training coach MCP server backed by Garmin Connect

概览

AI 生成的概览

本地 MCP 服务器,让助手基于 Garmin Connect 真实数据充当训练教练,制定受负荷与伤病规则约束的训练计划。

可以做什么
  • 读取 Garmin 数据:体能指标、分项历史、强度、准备度、个人纪录与活动区间
  • 规划训练:周计划、多周训练方案、课表预览、周期化阶段,并把结构化训练推送到手表
  • 管理运动员资料:档案、FTP 与阈值配速、基线及体能历史刷新、入门引导
  • 处理比赛、力量训练与伤病,包括伤病诊断与状态更新
  • 保存教练记忆:决策、运动员反馈、异常处理与待批准提案
  • 生成简报与签到,并提供规划、复盘与教练准则的提示词和资源
适用场景
适合已在 Garmin Connect 记录训练、希望助手依据真实历史制定计划、遵守负荷与伤病限制,并在多次对话间记住既往教练决策的耐力运动爱好者。
运行要求
通过 uvx 或 pip 本地运行(需 Python 3.12+),也可从源码运行。需要免费的 Garmin Connect 账号,并设置环境变量 GARMIN_EMAIL 与 GARMIN_PASSWORD;可选 COACH_DATA_DIR 指定数据存放位置。需要 Claude Code、Claude Desktop 或 Cursor 等 MCP 客户端,且须先用安装向导创建运动员档案,规划类工具才能使用。
安装前请注意
Garmin 账号密码以明文形式通过 GARMIN_PASSWORD 写入客户端配置或本地 .env 文件,OAuth 令牌也会缓存在磁盘上。服务器会向 Garmin 写入计划和训练,并修改本地 JSON 数据文件。凭据与令牌会发送给 Garmin 接口,对话内容会发送给所用大模型服务商,研究比赛、伤病或动作时还会抓取公开网页。每个数据目录仅支持一名运动员。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Coach Mcp,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

coach-mcp

[CI] [License: MIT]

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:

bash
uvx garmin-coach-mcp

Jump to Connect your MCP client and use uvx as the command.

Option B: from source

bash
git clone https://github.com/snoozelieb/coach-mcp.gitcd coach-mcp
python -m venv .venv# Linux/macOS:source .venv/bin/activate# Windows:.venv\Scripts\activate
pip install -r requirements.txtcp .env.example .env   # then edit: GARMIN_EMAIL, GARMIN_PASSWORDpython 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:

powershell
$env:GARMIN_EMAIL = "[email protected]"$env:GARMIN_PASSWORD = "your-password"
bash
export [email protected] GARMIN_PASSWORD=your-password

Claude Code

bash
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):

json
{  "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):

json
{  "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):

json
{  "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

  1. Create your profile. Run the interactive wizard (installed copies have the console script; a source checkout can run the script):

    bash
    garmin-coach-setup                 # pip / uvx installpython scripts/setup_wizard.py     # source checkout

    Until a profile exists the server runs in setup mode: plan, program and push writes answer setup_required and 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:

    bash
    echo '{"personal":{"name":null},"injury_history":[],"life_constraints":{}}' > data/athlete.jsonecho '{"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
    garmin-coach-login                 # pip / uvx installpython scripts/garmin_login.py     # source checkout

    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. Multi-week programs (training_program) are rejected if their starting capacity contradicts your actual history.
  4. 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.
  5. 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.
  6. Everything persists — decisions, approvals, adaptation patterns, and season lifecycle (race debriefs, phase transitions) live in local JSON and carry across sessions.

MCP surface

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

CategoryTools
Coaching coreget_coaching_snapshot (canonical, sectioned), get_compliance_report, get_coaching_score
Planningget_weekly_plan, update_weekly_plan, preview_workout (render one session the way the watch will show it, or fetch the session contract), push_plan_to_garmin (preview with the step tree per session, then confirm), 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 dataquery_metrics (kind=fitness/sport_history/intensity/daily/readiness/personal_records), get_activities_range
Athleteget_athlete, update_athlete, set_ftp, set_threshold_pace, analyze_ftp_test, refresh_athlete_baseline, refresh_fitness_history, backfill_history, get_onboarding_guide
Methodologyget_methodology, update_methodology
Racesraces (action=list/add/update/research), remove_race
Strengthsync_strength_session, get_strength_baseline, approve_progression, set_exercise_preference, generate_strength_workout, add_exercise
Injuriesdiagnose_injury, research_injury, update_injury_status
Researchresearch_exercise, list_exercises, research_sport
Memorylog_coaching_decision, get_active_decisions, update_decision_status, record_athlete_response, get_response_patterns, resolve_anomaly
Approvalspropose_coaching_action, list_pending_approvals, approve_proposal, reject_proposal
Interactivegenerate_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_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. 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

bash
# HTTP transport (streamable-http) instead of stdioCOACH_TRANSPORT=streamable-http FASTMCP_PORT=8000 garmin-coach-mcp
# Code Mode (search/execute meta-tools instead of 52 individual tools)pip install fastmcp[code-mode]COACH_CODE_MODE=1 garmin-coach-mcp
# Standalone morning auditpython 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.txtpython -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

MIT

来源:README.md,提交 7fb4163

工具

0
SourceWeft 曾在隔离沙箱中以无凭据方式启动该软件包一次,以列出其工具,但未能完成:软件包安装失败。

版本历史

2
  1. v1.3.0Oct 26, 2026
  2. v1.0.0Sep 16, 2026

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