Schema | apple-health-export-mcp

apple-health-export-mcp

Describes the environment variables required to run the server.

Name	Required	Description	Default
`AH_DB_PATH`	Yes	Path to the SQLite database file containing Apple Health data.

Features and capabilities supported by this server

Capability	Details
`tools`	{ "listChanged": true }
`logging`	{}
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`extensions`	{ "io.modelcontextprotocol/ui": {} }
`experimental`	{}

Functions exposed to the LLM to take actions

Name	Description
list_typesA	List which health metrics exist in this export, with row counts and date spans. Call this first to discover the exact `type` strings to pass to `get_quantity`.
list_sourcesA	List which sources/devices wrote a metric, with counts and date spans. Use it to see why a `sum` differs across devices, or to pick a `source` for `get_quantity`.
get_quantityA	Aggregate a numeric metric (steps, weight, heart rate, energy…) over a date range. Call `list_types` first to find the exact `type` string. Pick `agg` by metric kind: `sum` for cumulative (steps, active energy), `avg` for sampled (weight, heart rate). `sum` auto-deduplicates parallel devices (Watch + iPhone + apps) per day, so it does not over-count (ADR-0010); pass `source` to force one device. `avg/min/max` are not deduped. Returns [{period, value, n}].
get_sleepA	Per-night sleep stage durations (minutes), attributed to the wake-up day. Returns [{night, asleep_min, rem_min, deep_min, core_min, awake_min, in_bed_min}]. Older nights may report only in_bed_min (legacy devices lack stage detail).
get_workoutsA	Workout summary per activity type in a date range: count, total & avg minutes.

Interactive templates invoked by user choice

Name	Description
`daily_summary`	A single day's health snapshot: steps, sleep, resting heart rate, workouts, energy.
`weekly_review`	Calendar-week review (Mon–Sun): training load, sleep, recovery, and a recommendation.
`monthly_summary`	A month in review: totals, averages, body-weight change, and trend.
`yearly_summary`	A year in review: big-picture fitness trajectory and milestones.
`readiness_check`	Should I train hard today? Based on last night's sleep and recent recovery markers.
`sleep_report`	Sleep deep-dive over a range: duration, stage breakdown, and consistency.

Contextual data attached and managed by the client

Name	Description
No resources

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