apple-health-export-mcp
Query your Apple Health data including sleep, heart rate, steps, workouts, and more by ingesting an exported XML archive into a local SQLite database.
README
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 (
.gitignoreexcludes*.zip/*.db).
How it works
export.zip ──(ingest, one-time ~1-3 min)──► health.db (SQLite) ──◄── MCP server queries
Setup
-
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
uvxon 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) andapple-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.zip -
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 yourPATH.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 addequivalent: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
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。