Skip to main content
Glama

TrainBud

Talk to your training data.

TrainBud is an open-source MCP server that connects your Garmin Connect fitness data to Claude, Cursor, and other AI assistants. Ask about workouts, sleep, heart rate, recovery, and body composition in plain English — privately, on your machine.

Disclaimer: TrainBud is an unofficial community project. It is not affiliated with, endorsed by, or sponsored by Garmin Ltd. Garmin Connect is a trademark of Garmin Ltd.

npm CI License: MIT Node 22.12+ Trainbud MCP server — quality and maintenance score on Glama

Trainbud MCP server — license, quality, and maintenance card on Glama

Try it

Once connected to your MCP client, ask things like:

  • "What did I do today?"

  • "How's my sleep been this week?"

  • "Am I recovered enough to train hard tomorrow?"

  • "Is my resting heart rate trending down?"

See examples/prompts.md for more ideas.

Related MCP server: Garmin Connect MCP Server

Why TrainBud

  • Private — credentials stay in your local .env; data is cached on your machine

  • Local-first — SQLite cache, session tokens in .trainbud/

  • Works everywhere — Windows, macOS, Linux (Node.js 22.12+)

  • Any MCP client — Claude Desktop, Cursor, and other stdio-compatible clients

  • Smart fetching — batched API calls and automatic re-auth when sessions expire

Quick start

npx trainbud setup

The setup wizard walks you through credentials, authentication, and connecting Cursor or Claude Desktop — no MCP config editing required. Then restart your MCP client and ask it what you did today.

To keep trainbud on your PATH instead of typing npx every time:

npm install -g trainbud
trainbud setup

Needs Node 22.12+. Full walkthrough: QUICKSTART.md

git clone https://github.com/Zsadigzade/trainbud.git
cd trainbud
npm install
npm run build
npm link          # puts `trainbud` on your PATH; undo with `npm unlink -g trainbud`
trainbud setup

Without npm link, every trainbud <command> in this README is node dist/index.js <command> run from the repo root. If dist/ does not exist yet, run npm run build first.

Install as a Claude Code plugin — skills and MCP server in one step:

/plugin marketplace add Zsadigzade/trainbud
/plugin install trainbud@trainbud

Set credentials, then restart Claude Code:

export GARMIN_EMAIL="your@email.com"
export GARMIN_PASSWORD="yourpassword"

Command

What it does

/trainbud:trainbud-setup

First-time setup and diagnostics

/trainbud:trainbud

Ask about workouts, sleep, recovery, HR, stress, VO2 max

Plugin files live in plugin/. See plugin/README.md.

Claude Code skills (in-repo)

This repo also ships project skills in .claude/skills/ for development without installing the plugin:

Command

What it does

/trainbud-setup

Install, authenticate, configure MCP, run live check

/trainbud

Ask about workouts, sleep, recovery, HR, stress, VO2 max

Open the repo in Claude Code (claude in this directory) — skills load automatically.

To use skills in every project without the plugin, copy them to ~/.claude/skills/.

After setup, restart your MCP client and try /trainbud with "What did I do today?"

Dashboard

trainbud serve hosts a dashboard at /dashboard. It is phone-first, because the pairing flow is: you are standing next to the watch holding a phone when you approve a code.

It shows what stands out today against your own baselines, this week against last week, and resting heart rate and sleep plotted against your own 30-day median — all read from the local history store, so it paints instantly and works even when your Connect session has expired. A break in a line is a day with no measurement, not a zero.

It is also where you tell TrainBud who it is talking to:

Setting

What it changes

Name, units, primary sport, weekly goal

Every renderer, and what the AI is told about you

Thresholds

Where green becomes amber and amber becomes red — on the watch too

Watch cards

Which cards appear on the wrist and in what order, live on the next sync

AI model, tone, answer length

How the Ask card and the daily insight sound

Your own Ask questions

Up to five, 32 characters each. They lead the watch's Ask menu; the rest of the slots stay generated from what fired

Monthly spending cap

Optional. Refuses an Ask past the cap instead of spending past it

Privacy

Local feature counters, on by default, with a delete button

Usage. TrainBud runs on your own AI provider key, so every question and every daily insight is charged to you. The dashboard shows the tokens and cost per call, the month to date, and a 30-day chart. A model this build has no published price for is recorded with its cost left unknown rather than as zero — a call priced at zero would make a cap that can never trip.

Nothing on this page leaves your machine. There is no endpoint to send it to.

Garmin watch widget (Connect IQ)

View recovery, sleep, activity, stress, and VO2 max on your Garmin watch via a Connect IQ widget in ciq/.

Requires: trainbud serve running + HTTPS tunnel (same setup as web AI).

  1. Start the server and tunnel:

    trainbud serve
    cloudflared tunnel --url http://127.0.0.1:3847
  2. Build and sideload the widget — see ciq/README.md

  3. In Garmin Connect Mobile → Connect IQ → TrainBud settings, set:

    • Server URL — your tunnel URL (e.g. https://abc.trycloudflare.com)

  4. Open the widget on your watch — it shows a pairing code. Approve it in the dashboard (/dashboard?token=YOUR_API_KEY) to complete setup. The dashboard swaps that token for a session cookie and drops it from the URL, so the address bar is safe to screenshot afterwards.

The glance shows recovery and sleep, or the top finding in a few characters ("RHR +4 bpm"), from the last cached summary, so it renders without waiting on the network; once that is more than two hours old it says so. Open it and tap or swipe to cycle through the cards you left switched on in the dashboard. The watch calls GET /api/watch — a compact JSON summary, not the full MCP protocol.

On the Today card, START (or tap) opens a finding to the rule it fired on — "Each of the last 3 days was at least 3 bpm and 2 deviations above your 28-day median of 48 bpm" — and from there it can be muted for three days (POST /api/mute). The Sleep card names what moved last night against your own nights ("Deep 40m (1h24m)"). On the Forerunner 55, whose widget has 64 KB, those two are compiled out so the rest still fits.

Detector rules

Every finding fires on a rule you can see and move. The dashboard's Detector rules section holds the bars — resting HR days/bpm/deviations, sleep-debt hours, HRV drop, the training-load ratios, and "several signals together" (resting HR up, HRV down and sleep stress up at once) — bounded so no setting makes every day, or no day, a finding. Every surface shows the rule a finding fired on, with the numbers that were in force.

Voice (/voice)

Hold a button on your phone, ask a question out loud, hear the answer read back. Open https://YOUR-HOST/voice?token=YOUR_API_KEY once; it trades the key for a cookie and redirects to a clean URL.

  • Android / Chrome — speech recognition happens in the browser. Free, no key.

  • iPhone / Safari — records audio and transcribes it server-side via Groq (whisper-large-v3-turbo, about $0.04 per hour of audio). Add the key on the dashboard.

  • Both — answers are spoken back, and Read me my day / Read today's insight cost nothing at all: they are composed in code from data already on hand, not generated.

Not a watch feature and it cannot be one: Connect IQ exposes no microphone or speaker to a widget. Not hands-free either — mobile browsers suspend the microphone when the tab is backgrounded, so the phone has to be awake with the page in front of you.

Full detail: docs/VOICE.md

Always on (/voice and the watch, without babysitting a terminal)

trainbud serve in a terminal lasts until the terminal closes. To have the server and the tunnel come back by themselves after a reboot:

.\scripts\install-always-on.ps1 -Hostname trainbud.example.com

That registers a scheduled task for the server and a watchdog that checks both halves every five minutes — because a tunnel that answers is not a server that answers: a tunnel with nothing behind it serves its own error page under a 200, so the link looks alive while the watch reports HTTP -400. systemd, launchd and Docker Compose recipes are in the guide.

Full detail, including Cloudflare named tunnels: docs/ALWAYS-ON.md

Connect to Claude Desktop

Edit claude_desktop_config.json:

Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "trainbud": {
      "command": "node",
      "args": ["C:/path/to/trainbud/dist/index.js", "start"],
      "env": {
        "GARMIN_EMAIL": "your@email.com",
        "GARMIN_PASSWORD": "yourpassword"
      }
    }
  }
}

With trainbud on your PATH (npm install -g trainbud, or npm link from a clone), point the client at the command instead of a path:

{
  "mcpServers": {
    "trainbud": {
      "command": "trainbud",
      "args": ["start"]
    }
  }
}

Restart your MCP client, then start asking questions.

Tools

Tool

What it answers

get_latest_activity

Your most recent workout — distance, pace, HR, elevation

get_activities_range

Activities between two dates

get_sleep_data

Sleep duration, stages, score, awakenings — and which parts of last night were unusual for you

get_heart_rate_trends

Resting, max, and average HR over time

get_recovery_status

Recovery score from HRV, sleep, stress, resting HR

get_body_composition

Weight, body fat, and muscle mass trends

get_stress_levels

Daily stress averages and trends

get_vo2_max_trends

VO2 max fitness trends over time

get_training_insights

Combined weekly summary (activities, sleep, recovery, stress)

get_findings

What stands out against your own 28-day baselines, with the rule each finding fired on

get_week_review

This week against last, the load forecast, sleep debt, and your next race

compare_workouts

One workout against your own earlier efforts of the same type and distance

remember_context

Record a goal, a race and its date, an injury, or a note

get_user_context

What is on record about you, on any date

log_subjective

How a session actually felt — RPE, soreness, mood

CLI

trainbud setup          # Interactive first-time setup (recommended)
trainbud serve          # Remote HTTP MCP for web AI (claude.ai, ChatGPT)
trainbud check          # Live diagnostics against all tools
trainbud doctor         # What the watch would see: public URL, AI key, history depth
trainbud backfill       # Pull Garmin history into the local store (resumable)
trainbud findings       # What stands out against your own baselines, and the rule each fired on
trainbud rederive       # Rebuild sleep measurements (deep, REM, awakenings) from the local archive — no Garmin requests
trainbud start          # Start the MCP server (stdio)
trainbud auth           # Force re-authentication
trainbud cache clear    # Clear cached data
trainbud devices        # List paired watches
trainbud devices revoke <id>   # Take one watch's access away
trainbud rotate status  # Which secrets are set, and whether their two homes agree
trainbud rotate api-key # Replace TRAINBUD_API_KEY, and say what that invalidates
trainbud rotate ai-key  # Replace the AI key in BOTH .env and app.db
trainbud status         # Show session and cache status
trainbud --version      # Print version

rotate ai-key writes both stores on purpose. The AI key lives in .env and in app.db, the database copy is the one actually read, and hand-editing only the file leaves a rotated key that never took effect.

Every one of these also works as npx trainbud <command> without installing anything.

Troubleshooting: trainbud: command not found

Either use npx trainbud <command>, or install it globally with npm install -g trainbud. Running from a clone instead? Run npm link once from the repo root, or call the built entry point directly with node dist/index.js doctor (after npm run build).

Configuration

Variable

Default

Description

GARMIN_EMAIL

—

Garmin Connect email

GARMIN_PASSWORD

—

Garmin Connect password

TRAINBUD_SESSION_PATH

.trainbud/session.json

Session token storage

TRAINBUD_LOG_PATH

.trainbud/mcp.log

Log file path

TRAINBUD_CACHE_PATH

.trainbud/cache.db

SQLite cache database

CACHE_TTL_ACTIVITIES

1800

Activity cache TTL (seconds)

CACHE_TTL_SLEEP

7200

Sleep cache TTL (seconds)

CACHE_TTL_STATS

3600

Stats cache TTL (seconds)

TRAINBUD_API_KEY

auto-generated

Bearer token for HTTP MCP (trainbud serve)

TRAINBUD_HOST

127.0.0.1

Bind host for HTTP server

TRAINBUD_PORT

3847

Bind port for HTTP server

Security & privacy

  • Credentials live only in your local .env file — never sent to a third party

  • Session tokens in .trainbud/session.json are as sensitive as a password

  • Tool errors are sanitized before reaching the AI client

  • Uses the unofficial garmin-connect npm package (not Garmin's enterprise OAuth API)

  • MFA is not supported by the underlying library — disable MFA or use an app-specific password

  • The server binds 127.0.0.1 by default. It is only reachable from the internet if you point a tunnel at it, and every route except /health needs the API key

  • The dashboard takes the key once, on /dashboard?token=…, then trades it for an HttpOnly session cookie and redirects to a clean URL — so the key does not sit in your address bar, your history, or a screenshot

  • A paired watch holds a token scoped to that watch, minted at pairing and stored on the server as a SHA-256 hash. trainbud devices lists them, trainbud devices revoke <id> takes one away — without logging out the dashboard, /mcp, or your other watches. A watch paired before 0.5.2 holds the API key itself; re-pair it to swap that for a scoped token

  • Every response carries Content-Security-Policy, X-Content-Type-Options, X-Frame-Options and Referrer-Policy, including the 401s. HSTS is sent only on a request that actually arrived over TLS, so the loopback dashboard stays reachable

What TrainBud is not

  • Not a hosted service. There is no TrainBud account and no TrainBud server. You run it, on your machine, against your own Garmin credentials

  • Not an official Garmin integration. It drives an unofficial library against the Connect web API. Garmin can change that API without notice, and does

  • Not MFA-compatible. If your Connect account has MFA on, this will not log in

  • Not free to ask. The AI features run on your own Anthropic key and are billed to you. The dashboard meters every call and can refuse past a cap you set

Troubleshooting

Issue

Fix

Authentication failed

Verify .env credentials, run trainbud auth

MFA enabled on account

Disable MFA or use an app-specific password

Stale data

Run trainbud cache clear

Rate limited

Wait 60 seconds; cached responses are used when available

Watch shows "Not a TrainBud server" or error -400

Your public URL is answering with something that is not TrainBud's JSON — usually a tunnel that is down. Run trainbud doctor; it says exactly what came back

Watch shows "Watch not authorised"

The API key changed since the watch paired. Pair it again from the dashboard

Watch shows "AI not set up"

AI is bring-your-own-key. Paste an Anthropic key into the dashboard

No sleep/HR data

Ensure your Garmin device has synced to Garmin Connect

Server won't start

Check that GARMIN_EMAIL and GARMIN_PASSWORD are set in .env

Docker

A published image, built and exercised in CI on every push so it is not a promise that quietly rots:

docker pull ghcr.io/zsadigzade/trainbud:latest

Or build it yourself from this repository:

docker build -t trainbud .

One thing matters more than the rest: mount a volume at /app/.trainbud. That is where the SQLite history, the cached session and the log live. Without it, every container restart throws away your downloaded history and re-authenticates from scratch.

As an MCP server for a desktop client

The default entrypoint speaks MCP over stdio, so the client runs the container:

{
  "mcpServers": {
    "trainbud": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "GARMIN_EMAIL", "-e", "GARMIN_PASSWORD",
        "-v", "trainbud-data:/app/.trainbud",
        "trainbud"
      ]
    }
  }
}

-i is required — without it the container has no stdin and the client sees a server that connects and immediately goes quiet. The two -e flags with no value pass the variables through from your own environment, so your credentials stay out of the config file.

As an HTTP server, for the watch app or a web client

docker run --rm -p 3847:3847 \
  -e GARMIN_EMAIL -e GARMIN_PASSWORD \
  -e TRAINBUD_API_KEY \
  -e TRAINBUD_HOST=0.0.0.0 \
  -v trainbud-data:/app/.trainbud \
  --entrypoint node trainbud dist/index.js serve

TRAINBUD_HOST=0.0.0.0 is needed inside a container: the server binds 127.0.0.1 by default, which is the right default on a laptop and unreachable from outside a container.

Filling the history

trainbud setup is interactive and assumes a terminal, so with Docker do the one-off backfill directly:

docker run --rm -e GARMIN_EMAIL -e GARMIN_PASSWORD \
  -v trainbud-data:/app/.trainbud \
  --entrypoint node trainbud dist/index.js backfill

The same pattern runs doctor, check and devices.

Development

npm install
npm run build
npm test          # the whole suite, via the Node test runner
npm run lint
npm run dev       # Start with auto-reload

Use .nvmrc with nvm/fnm for Node 22. npm run test:coverage reports coverage through Node's own test runner, and npm run test:watch reruns on change.

See CONTRIBUTING.md and docs/VAULT.md for architecture and design notes (Obsidian vault, outside this repo).

Roadmap

  • VO2 max trends

  • Stress levels

  • Training insights

  • Workout comparison

  • Docker image — published to ghcr.io/zsadigzade/trainbud

License

MIT — see LICENSE.

Garmin Connect is a trademark of Garmin Ltd. This project is not affiliated with Garmin Ltd.

Available Tools

15 tools
compare_workoutsB

Compares one workout against the user's own earlier workouts of the same type and a similar distance (or duration, for sports that record no distance). Reports duration, pace, heart rate, elevation and calories against the closest previous effort, with the typical value across the comparable set. Defaults to the most recent activity. Use this when asked how a session went compared with usual, rather than reading a single activity summary.

Inputs: limit, activity_id.

get_activities_rangeA

Returns Garmin activities within an ISO 8601 date range.

Inputs: end_date (required), start_date (required).

get_body_compositionC

Returns weight, body fat, and muscle mass trends over a time period.

Inputs: days.

get_findingsA

Returns what stands out in the user's stored history against their own 28-day baselines: resting heart rate elevation, sleep debt, HRV trend breaks, and training load ratio. Prefer this over reading raw metrics when asked how things are going.

Inputs: no named parameters.

get_latest_activityA

Returns the most recent Garmin activity with distance, duration, pace, and heart rate stats.

Inputs: no named parameters.

get_recovery_statusB

Combines HRV, sleep, stress, and resting heart rate into a recovery score and training recommendation.

Inputs: hrv_weight, sleep_weight, stress_weight, resting_hr_weight.

get_sleep_dataB

Returns sleep duration, quality score, stage breakdown, and interruptions for recent nights.

Inputs: nights.

get_stress_levelsC

Returns daily stress averages and trends from Garmin Connect.

Inputs: days.

get_training_insightsC

Returns a combined weekly training summary: latest activity, recent workouts, sleep, recovery, and stress.

Inputs: days.

get_user_contextA

Returns the goals, races, injuries and notes on record for the user. Read this before giving training advice.

Inputs: on_date, include_closed.

get_week_reviewA

Returns this training week against the previous one — sessions, training load, sleep, resting heart rate, HRV and stress — plus where the acute:chronic load ratio lands if next week repeats this one, a sleep debt and consistency read against the user's own baseline, and the next race on record. Prefer this over reading individual metrics when asked how the week or the block is going, whether to back off, or what to do next week.

Inputs: no named parameters.

log_subjectiveA

Records how a day or session actually felt, rated 1-10: perceived effort (rpe), soreness, or mood. Garmin has no equivalent.

Inputs: kind (required), value (required), date, note.

remember_contextA

Records something about the user that Garmin does not know: a goal, a race and its date, an injury, or a free-form note. Use this whenever the user mentions one in passing. It can also silence a finding the user has already accounted for — see mutes.

Inputs: kind (required), text (required), effective_to, effective_from.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.6/5.0

    Scored across 15 tools

    Disambiguation4/5

    Most tools are clearly distinct, but some overlap exists between get_findings, get_recovery_status, get_training_insights, and get_week_review, which all return aggregated health/training metrics. Descriptions help clarify their specific focuses (anomalies vs. recovery score vs. weekly summary vs. week-over-week comparison), and get_latest_activity vs get_activities_range are distinct by scope. Overall, an agent can likely select the right tool with careful reading.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: most start with 'get_' for retrieval, with a few action verbs (remember_context, compare_workouts, log_subjective) that are equally clear. No mixed conventions or vague names—every name is self-explanatory.

    Tool Count5/5

    15 tools is at the upper end of the ideal range but well-scoped for a training assistant that combines user context, Garmin metrics, and comparative analysis. Each tool covers a distinct need, from retrieving raw data to high-level insights, without redundancy or bloat.

    Completeness4/5

    The surface covers the core lifecycle: user context (remember/get), subjective logging, comparisons, and a wide array of metric getters (sleep, HR, recovery, body comp, stress, VO2, activities). Minor gaps exist—no direct update/delete for context, and some overlapping insights—but these are not critical for the server's purpose and agents can work around them.

    Maintenance

    ActivitySlowing
    ResponsivenessNo recent community issues

    Related MCP Connectors

    Related MCP Servers