Fettle

fettle

fettle (n.) — condition, shape: "in fine fettle."

Own your Fitbit / Pixel health data. fettle syncs it from the Google Health API into a local SQLite file, computes transparent versions of the "Premium" metrics (readiness, sleep score, training load), and puts a dashboard, an insights engine, and a zero-cost AI coach on top — all running on your machine, with your data never leaving it except to talk to Google.

> The Google Health API replaces the legacy Fitbit Web API (full shutdown Sept 2026). > This project is built for personal, single-user use: your OAuth consent screen > runs in Testing mode with yourself as the only test user, so there is no > third-party security review and no server component.

Screenshots

All screenshots show the app running against real synced data.

Overview — today's readiness score, its component drivers, the 28-day history, and goal status:

AI coach — answers include live inline widgets. A single question here produces a metric-history chart, a stat tile, and a peer-benchmark band, each rendered and fetched by the frontend:

Tool orchestration and the daily briefing — follow-up questions continue the session. The tool chips show each engine call behind the dual-axis comparison, and the reply frames the correlation (r = -0.50) as an association, not a cause. Right: the briefing generated after each sync from the engine's computed evidence:

Metric drill-down and command palette — per-metric statistics (7/28-day averages, range, personal best) for every synced type, and ⌘K fuzzy search across all 40 registered metrics with live sparklines in the results:

Workout drill-down, and the coach on a session — any session expands into its heart-rate trace with %HRmax time-in-zone; asked about a workout, the coach pulls the session log, renders the day's trace inline, and folds remembered context (an injury mentioned in an earlier chat) into the advice:

Coach memory and the weekly retrospective — the Memory panel lists every durable fact the coach has saved, each removable; Sundays close the week with a written week-over-week review:

Sleep analysis and peer benchmarks — stage mix against published targets, 14-night sleep debt and consistency; reference bands with the next threshold annotated and every value cited:

Workouts and goals — weekly volume, 30-day activity mix, and the per-session log; goal cards with streaks and 28-day adherence, sorted by status:

Light theme — follows the system setting, with a manual override:

What's inside

• Sync engine — incremental per-type watermarks over 30+ Google Health data types:

daily rollups, intraday samples (sub-minute heart rate, SpO2, HRV), and full sleep / exercise sessions, including per-workout detail.

• Dashboard (Next.js) — Overview with a readiness hero and your goals, then

Insights, Sleep deep-dive, Workouts, Standing (peer benchmarks), and a Metrics drill-down for every synced type. Light/dark themes, ⌘K command palette, and deep-linkable state (?v= view, ?m= metric drawer, /coach?c= conversation, ?theme= override).

• Derived metrics, formulas in the open — Readiness (0–100 recovery index vs your

own 28-day baseline), Sleep Score, TRIMP-style Cardio Load. Every threshold and weighting traces to a citation in [docs/health-metrics-spec.md](docs/health-metrics-spec.md).

• Insights engine — deterministic detectors: trends, z-score anomalies, ACWR

training-load balance, 14-night sleep debt, Spearman correlations (honestly framed as associations), goal streaks, and a vitals early-warning that only fires when ≥2 vitals drift together.

• Workout drill-down — every session row expands into its intraday heart-rate

trace, %HRmax time-in-zone, and a comparison against your median session of that activity.

• AI coach (/coach) — a ChatGPT-style chat over your data: conversation

history, attachments, model picker, streaming replies with inline generative widgets (charts, comparisons, intraday traces, readiness ring, sleep stages, benchmark bands, goals). The coach can create, update, and delete your goals — and it remembers: durable facts you mention (injuries, schedule, events) are saved, recalled at the start of each conversation, and listed in a Memory panel you can prune anytime.

• Daily briefing + weekly retrospective — after each sync, an analyst model

turns the day's computed evidence into a morning read (headline, narrative, 3–5 cards, every number traceable). Yesterday's briefing rides along as context, so narratives continue instead of restarting. Sundays add a week-over-week retrospective: what changed, which goals moved, one thing to fix next week.

• Notifications — after a scheduled sync, macOS notifications fire only when

something needs you: the 7-day token is about to die, several vitals drift together, or a goal streak you'd built breaks.

How the AI layer works

Next.js chat UI ──SSE──▶ FastAPI /api/chat ──subprocess──▶ opencode run (free Zen models)
                                                                 │ MCP (stdio)
                                                                 ▼
                                            backend/mcp_server.py — 25 typed tools
                                                                 │
                                                                 ▼
                                            SQLite + the deterministic analysis engine

• The app never holds an LLM API key. It shells out to the opencode

CLI you're already logged into, using opencode Zen's free models, so there is no per-conversation cost.

• backend/mcp_server.py exposes the analysis engine as 25 MCP tools (12 read,

5 write — goals and memory — and 8 display). Metric arguments are closed enums generated from the data-type registry, so the model cannot hallucinate a metric name.

• The LLM orchestrates and narrates; it never does the math. Trends, anomalies,

correlations, and scores all come from the deterministic engine — the model's job is to call the right tools and explain the results.

• Display tools (show_chart, show_readiness, …) return only an acknowledgement;

the SSE bridge turns them into widget events and the frontend renders live Recharts components in place, exactly where the model called them.

• The briefing is the same idea inverted: the engine computes an evidence pack, a

tool-less analyst agent returns strict JSON, the backend validates it (real metric names, capped cards) and caches it by evidence digest so unchanged data never re-generates.

Setup

Prerequisite: a Fitbit account migrated to Google (mandatory since 2026-05-19) with data flowing into the Google Health / Fitbit app, plus Python 3.11+ and Node 18+.

Quickstart

git clone https://github.com/Deekshith-Dade/fettle.git && cd fettle
ops/bootstrap.sh        # venv + all dependencies (safe to re-run)
ops/dev.sh              # backend :8400 + dashboard :3400 — Ctrl-C stops both

Open http://localhost:3400. On a fresh install the dashboard opens as a setup wizard that walks you through the one-time Google part — creating your own free Cloud project, minting an OAuth client, connecting, first sync — and checks off each step live as you go. About ten minutes, once.

> Why my own Google Cloud project? Every Google Health API scope is restricted: > shipping one shared app to the public would require Google's OAuth verification plus > an annual paid security assessment. Personal apps instead use the console's > Testing lane — your own project, your own keys, yourself as the only user. Your > data flows from Google straight to your machine; no one else's server or OAuth > client is ever involved.

> ⚠️ The one recurring chore: Testing-lane refresh tokens expire every 7 days. > The dashboard counts down in the top bar, the briefing warns you before it dies, and > reconnecting is a one-click handshake — nothing to re-tick. A scheduled sync exits > with code 2 when the token has died.

What the wizard walks you through

For the curious — or the wizard-averse; every step also works headless:

1. Create a Google Cloud project (free, any name) and **enable the Google Health

API** in it.

1. OAuth consent screen: user type External, publishing status left at

Testing; add your own Gmail under Test users; under Data access, add the four googlehealth.*.readonly scopes (activity, health metrics, sleep, nutrition — the wizard has a copy-all button).

1. Create an OAuth client (type Web application) whose only authorized redirect

URI is http://localhost:8400/auth/callback, and download its JSON.

1. Hand over the JSON — pasted or dropped into the wizard, validated (with a

warning if the redirect URI looks wrong), stored as backend/credentials.json (gitignored).

1. Connect Google — one consent screen; tick every scope box.
2. First sync — ~90 days of dailies plus recent full-resolution intraday, then

straight into the dashboard.

Terminal equivalent: cd backend && .venv/bin/python cli.py auth then cli.py sync; cli.py status shows per-type watermarks, cli.py sync steps sleep syncs specific types. Moving machines? Copy backend/credentials.json + backend/token.json across and skip straight to sync.

Running by hand (what dev.sh does)

cd backend  && .venv/bin/python -m uvicorn app.main:app --reload --host :: --port 8400
cd frontend && npm run dev -- -p 3400

--host :: binds IPv4 + IPv6. Without it uvicorn is IPv4-only and Safari — which resolves localhost to ::1 first — loads the dashboard but never fills it in. Ports and origins all default correctly now; backend/.env.example and frontend/.env.example document the overrides if you need different ones.

4. AI coach (optional — everything else works without it)

# Install opencode and log in once (the free opencode Zen tier is enough):
curl -fsSL https://opencode.ai/install | bash    # or: brew install sst/tap/opencode
opencode auth login

# The MCP server needs its own venv — the `mcp` package's dependencies (newer
# starlette/pydantic) conflict with the pinned FastAPI. Do NOT install mcp into
# the main backend venv.
cd backend
python3 -m venv .venv-mcp
.venv-mcp/bin/pip install mcp pydantic-settings

Nothing else to configure: opencode.json (repo root) registers the MCP server with checkout-relative paths, and the backend always launches opencode from the repo root. (Running opencode by hand? Do it from the repo root so those paths resolve.) The agent personas live in .opencode/agent/ (fettle-coach for chat, fettle-analyst for the briefings); both default to a free model, and the backend falls back automatically when the free-model lineup rotates.

Scheduled sync + notifications (optional)

ops/install-sync.sh     # generates the launchd job for this checkout and loads it

Syncs every 6 hours. Each run also refreshes the daily briefing (and the weekly retrospective on Sundays), then sends a macOS notification if — and only if — something needs attention: token about to expire, the multi-vital early-warning firing, or a broken goal streak.

Logs land in ~/Library/Logs/fettle-sync.log; exit code 2 in the log means the 7-day token died — reconnect from the dashboard. Remove the job any time with launchctl bootout gui/$(id -u)/com.fettle.sync.

Nightly backup to iCloud Drive (recommended)

health.db is the only copy of your coach memories, chat history, goals, and briefing archive — none of that can be re-pulled from Google.

ops/install-backup.sh   # nightly at 02:30 (runs on next wake if the lid was shut)
ops/backup.sh           # or take one snapshot right now

Each run VACUUMs a consistent snapshot, integrity-checks it, gzips it to iCloud Drive/fettle-backups/health-YYYY-MM-DD.db.gz (~8 MB), keeps the newest 14, and copies token.json/credentials.json alongside. Restore = stop the backend, gunzip -c > backend/health.db, restart.

One-time step: the first scheduled run makes macOS ask whether Python may touch iCloud Drive. Trigger it while you're at the screen and click Allow:

launchctl kickstart gui/$(id -u)/com.fettle.backup
tail ~/Library/Logs/fettle-backup.log   # expect "✓ backed up …"

If the log shows starting backup… with no ✓, the permission prompt was missed — grant it in System Settings → Privacy & Security → Full Disk Access (add the Python from backend/.venv/bin/python) and kickstart again. A blocked run kills itself after 10 minutes, so it can never wedge the schedule.

Access it from your phone (optional)

Put the machine on a Tailscale tailnet (brew install --cask tailscale-app, sign in, and install the Tailscale app on your phone with the same account). Then open http://..ts.net:3400 from anywhere — the dashboard derives its API base from whichever host served it, and the backend accepts private tailnet/LAN origins (cors_origin_regex in backend/app/config.py). Nothing is exposed to the public internet; it's WireGuard between your own devices.

One caveat: the Google OAuth redirect URI is localhost, so connecting/re-authing happens on the machine itself — do the weekly handshake there; reading and the coach work from any device.

Make it feel like an app: open the tailnet URL in Safari on the phone, then Share → Add to Home Screen. fettle installs as a standalone app — its own icon and app-switcher card, no browser chrome, no rubber-band scroll, content tucked around the notch. It refreshes its data automatically when foregrounded after time away (there's no reload gesture in standalone mode). iOS is happy to install over plain http:// on a tailnet; no HTTPS or service worker required.

Repo map

backend/
  app/
    config.py           Settings + the data-type registry (the single source of truth)
    auth.py             Google OAuth flow, token storage, auto-refresh
    health_client.py    Thin client over the Health API (list + dailyRollUp)
    store.py            SQLite schema, upserts, query helpers
    sync.py             Incremental sync engine + derived-metric processors
    readiness.py        0–100 recovery index vs your 28-day baseline
    insights.py         Deterministic detectors (trends, anomalies, ACWR, correlations…)
    sleep_analysis.py   Stage mix vs targets, debt, consistency
    benchmarks.py       Peer-norm bands ("Standing")
    goals.py            Goal CRUD + adherence evaluation
    coach.py            Deterministic day-plan recommendations
    briefing.py         Evidence packs → analyst model → daily briefing + weekly retro
    workouts.py         Per-session detail: intraday HR trace + time-in-zone
    notify.py           Post-sync macOS alerts (token / vitals / broken streaks)
    chat.py             SSE bridge: /api/chat ↔ opencode CLI (tools → widgets)
    chat_store.py       Conversation + message persistence
    backup.py           Consistent snapshot of health.db (+tokens) to iCloud Drive
  mcp_server.py         The 25 MCP tools the coach model calls
  cli.py                auth / sync / status / backup commands
  tests/                Regression net over the stats engine (pytest)
frontend/
  app/page.tsx          The dashboard (all views)
  app/coach/page.tsx    The coach chat page
  components/           chat UI, generative widgets, insights views, ⌘K palette
docs/
  health-metrics-spec.md  The cited evidence base for every formula and threshold
ops/
  bootstrap.sh          One command from fresh clone to runnable app
  dev.sh                Start both servers (backend :8400, dashboard :3400)
  install-sync.sh       Generate + load the 6-hourly launchd sync for this checkout
  install-b

…

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Deekshith-Dade](https://github.com/Deekshith-Dade)
- **Source:** [Deekshith-Dade/fettle](https://github.com/Deekshith-Dade/fettle)
- **License:** MIT

Install and usage instructions live in the source repository linked above.