apple-health-export-mcp 0.1.1

MCP server to query an exported Apple Health archive (export.zip) via SQLite.

apple-health-export-mcp

An MCP server that lets an AI assistant query your Apple Health data — sleep, heart rate, steps, body mass, workouts, and any other metric in your export.

Apple has no live export API, so this works on a snapshot: you export your health archive once, ingest it into a local SQLite database, then the server answers queries against it. See docs/adr.md for why.

Privacy: your health data never leaves your machine and is never committed to git (.gitignore excludes *.zip / *.db).

How it works

export.zip ──(ingest, one-time ~1-3 min)──► health.db (SQLite) ──◄── MCP server queries

Setup

1. Export your data on iPhone: Settings → Health → tap your photo → Export All Health Data. AirDrop/save the resulting export.zip.

2. Install with uv. Installing once (rather than uvx on every launch) keeps the process tree shallow, which matters for clean shutdown — see Shutdown & process model.

   uv tool install apple-health-export-mcp
   # …or from source:
   uv tool install git+https://github.com/burakdirin/apple-health-export-mcp
   

   This puts two commands on your PATH: apple-health-export-mcp (the server) and apple-health-export-mcp-ingest.

3. Ingest the archive (one time per new export):

   AH_DB_PATH=~/.local/share/apple-health-export-mcp/health.db \
     apple-health-export-mcp-ingest ~/Downloads/export.zip
   

4. Add to your MCP client (e.g. Claude Code .mcp.json). The bare command starts the stdio server; point it at the DB you just built.

   Installed (recommended) — invoke the binary directly (no wrapper process):

   {
     "mcpServers": {
       "apple-health": {
         "command": "apple-health-export-mcp",
         "env": { "AH_DB_PATH": "/Users/you/.local/share/apple-health-export-mcp/health.db" }
       }
     }
   }
   

   Use an absolute path to the binary (which apple-health-export-mcp) if your client doesn't inherit your PATH.

   Or via uvx (no install; the bare package name runs the server):

   {
     "mcpServers": {
       "apple-health": {
         "command": "uvx",
         "args": ["apple-health-export-mcp"],
         "env": { "AH_DB_PATH": "/Users/you/.local/share/apple-health-export-mcp/health.db" }
       }
     }
   }
   

   claude mcp add equivalent:

   claude mcp add apple-health --env AH_DB_PATH=~/.local/share/apple-health-export-mcp/health.db \
     -- apple-health-export-mcp
   

Shutdown & process model

The server is a single process that shuts down on stdin EOF — the MCP spec's primary shutdown signal — so closing your client terminates it cleanly. Avoid extra wrapper layers (uv run … fastmcp run …): uv/uvx stay in the process tree as a parent and only conditionally forward signals, so a wrapped server can be orphaned when the client exits or you press Ctrl+C. Installing the tool and launching the binary directly (config above) gives the shallowest tree and the most reliable cleanup. fastmcp run fastmcp.json is for local dev only.

Tools

Tool	Returns
list_types()	Which metrics exist in your data + row counts and date spans (discovery)
list_sources(type)	Which devices/apps wrote a metric (counts, date spans)
get_quantity(type, start, end, agg, bucket, source)	Per day/week/month/all aggregate for a numeric metric (steps, HR, weight…)
get_sleep(start, end)	Per-night sleep stage durations
get_workouts(start, end)	Workout summary per activity type in the range

All query tools require a date range and return aggregates only — never raw rows (ADR-0008). get_quantity with agg="sum" auto-deduplicates parallel devices (Watch + iPhone + apps) so totals aren't inflated (ADR-0010); pass source (see list_sources) to force one device.

Prompts

Reusable coaching workflows the client can invoke (they orchestrate the tools and reply in your language):

Prompt	Purpose
daily_summary(day?)	One day's snapshot
weekly_review(week_of?)	Calendar week (Mon–Sun): load vs recovery + advice
monthly_summary(month?)	A month in review (YYYY-MM)
yearly_summary(year?)	A year's fitness trajectory (YYYY)
readiness_check()	Train hard today? From sleep + recovery markers
sleep_report(start?, end?)	Sleep duration, stages, consistency

Arguments are optional — they default to today / this week / this month / this year.

Development

uv sync
uv run pytest
uv run ruff check

License

MIT