Allows querying Apple Health data including sleep, heart rate, steps, body mass, workouts, and other metrics by ingesting an exported health archive into a local SQLite database.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@apple-health-export-mcp show my sleep stages for last night"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 (
.gitignoreexcludes*.zip/*.db).
export.zip ──(ingest, one-time ~1-3 min)──► health.db (SQLite) ──◄── MCP server queriesRelated MCP server: Apple Health MCP
Export your data on iPhone: Settings → Health → tap your photo → Export All Health Data. AirDrop/save the resulting export.zip.
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-mcpThis puts two commands on your PATH: apple-health-export-mcp (the server) and
apple-health-export-mcp-ingest.
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.zipAdd 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-mcpThe 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.
Tool | Returns |
| Which metrics exist in your data + row counts and date spans (discovery) |
| Which devices/apps wrote a metric (counts, date spans) |
| Per day/week/month/all aggregate for a numeric metric (steps, HR, weight…) |
| Per-night sleep stage durations |
| 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.
Reusable coaching workflows the client can invoke (they orchestrate the tools and reply in your language):
Prompt | Purpose |
| One day's snapshot |
| Calendar week (Mon–Sun): load vs recovery + advice |
| A month in review (YYYY-MM) |
| A year's fitness trajectory (YYYY) |
| Train hard today? From sleep + recovery markers |
| Sleep duration, stages, consistency |
Arguments are optional — they default to today / this week / this month / this year.
uv sync
uv run pytest
uv run ruff checkMIT
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}].
| Name | Required | Description | Default |
|---|---|---|---|
| agg | No | Aggregation: 'sum' for cumulative metrics (steps, energy), 'avg' for sampled ones (heart rate, weight). | sum |
| end | Yes | Local calendar date, ISO 'YYYY-MM-DD'. | |
| type | Yes | HealthKit type identifier. Discover valid values with `list_types`. | |
| start | Yes | Local calendar date, ISO 'YYYY-MM-DD'. | |
| bucket | No | Group results by this time bucket. | day |
| source | No | Restrict to one source/device (see `list_sources`). Omit to auto-dedupe parallel devices. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and idempotentHint. The description adds valuable behavioral context: auto-deduplication for sum with ADR-0010, no dedup for avg/min/max, and return format [{period, value, n}]. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Five sentences, well-structured and front-loaded. Every sentence adds necessary information. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all key aspects: type discovery, agg selection, dedup behavior, source parameter, return format. Has output schema implicitly mentioned. No gaps given the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds meaning beyond schema: explains auto-dedup for sum, that source forces one device, and return format. Examples of type strings are also helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool aggregates numeric metrics over a date range. It distinguishes from siblings by focusing on quantitative metrics and references list_types for type discovery. The verb 'aggregate' and resource 'numeric metric' are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to call list_types first for the exact type string. It also instructs on when to use sum vs avg based on metric kind and mentions dedup behavior. However, it does not explicitly exclude usage compared to sibling tools like get_sleep or get_workouts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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).
| Name | Required | Description | Default |
|---|---|---|---|
| end | Yes | Local calendar date, ISO 'YYYY-MM-DD'. | |
| start | Yes | Local calendar date, ISO 'YYYY-MM-DD'. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint and idempotentHint. Description adds that older nights may report only in_bed_min due to legacy devices, which is useful behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff. First sentence states purpose and return format; second addresses legacy data nuance. Efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given annotations, schema, and output schema existence, description provides sufficient context. It covers return format and legacy behavior, completing the picture for agent decision.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline applies. Description does not add parameter-specific meaning beyond schema, which already documents start and end dates as ISO strings.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description specifies 'Per-night sleep stage durations (minutes), attributed to the wake-up day.' It clearly identifies the resource (sleep stages) and the action (get durations). No sibling tool relates to sleep, so differentiation is inherent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for retrieving sleep stage data per night with wake-up day attribution. No explicit when-not or alternatives, but sibling tools are unrelated, so context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Workout summary per activity type in a date range: count, total & avg minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| end | Yes | Local calendar date, ISO 'YYYY-MM-DD'. | |
| start | Yes | Local calendar date, ISO 'YYYY-MM-DD'. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond the annotations: it reveals that results are aggregated per activity type and include count, total, and average minutes. The annotations already indicate read-only and idempotent nature, so the description enriches the agent's understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's output: 'Workout summary per activity type in a date range: count, total & avg minutes.' No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input schema and presence of an output schema, the description covers the key output fields and aggregation dimension. It is sufficiently complete, though it could explicitly note that all activity types present in the range are returned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds minimal extra meaning by mentioning 'in a date range', which aligns with the start and end parameters but does not provide new details beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides a workout summary per activity type with count, total, and average minutes over a date range. It is specific and distinguishes from sibling tools like get_sleep or list_types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not specify when to use this tool versus alternatives, nor does it state prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | HealthKit type identifier. Discover valid values with `list_types`. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's additional mention of 'counts and date spans' adds minor context about output content but no new behavioral traits. The bar is lower due to annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero wasted words. Front-loaded with the main purpose, then usage guidance. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter, complete annotations, and an output schema (implied by context signals), the description covers the key points: what it lists and why to use it. No missing information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of the single parameter with a clear description and example. The tool description does not add further parameter details, but schema coverage is high so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists sources/devices that wrote a metric, with counts and date spans. It differentiates from siblings like get_quantity and list_types by focusing on source metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use: to diagnose why a sum differs across devices or to pick a source for get_quantity. It doesn't list when-not-to-use, but the guidance is clear and practical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds value by hinting at output content (row counts, date spans), which is beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the main action, no unnecessary words. Every sentence serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple nature (no parameters, output schema present), the description adequately explains the tool's role in relation to get_quantity. Could mention potential use with other tools, but not required.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so the schema coverage is 100% by default. The description does not need to add param info; it appropriately focuses on the output and usage, meeting the baseline for zero-param tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists health metrics with row counts and date spans, distinguishing it from sibling tools like get_quantity, get_sleep, etc., by positioning itself as a discovery tool for type strings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises 'Call this first to discover the exact `type` strings to pass to `get_quantity`', providing clear context for when to use it. Could be more explicit about when not to use, but the guidance is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0get_quantityget_sleepget_workoutslist_sourceslist_typesScored across 5 tools
Each tool has a clearly distinct purpose: get_quantity aggregates numeric metrics, get_sleep handles sleep stages, get_workouts summarizes workouts, list_sources lists devices, and list_types enumerates health metrics. No overlap in functionality.
All tools follow a consistent verb_noun pattern using snake_case (e.g., get_quantity, list_sources). There is no variation in style or convention.
With 5 tools, the server is well-scoped for a health data export interface. Each tool serves a distinct purpose without redundancy, and the count is neither too sparse nor too heavy.
The tool surface covers key health data access: numeric metrics (get_quantity), sleep (get_sleep), workouts (get_workouts), source discovery (list_sources), and type discovery (list_types). This set supports common queries without obvious gaps for the domain.
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