Practical, no-fluff guides for getting your Apple Health data into AI agents, privately.
Apple Health holds years of your most personal data (heart rate variability, sleep stages, workouts, resting heart rate, blood oxygen, and 190 metrics in all), but it lives locked inside your iPhone, invisible to the AI tools you already use. These guides show you how to bridge that gap: how to export Apple Health data to clean JSON and feed it to large language models like Claude, ChatGPT, and Cursor so an agent can actually reason about your body, spot trends, and answer questions in plain language.
Everything here is built around a local-first, read-only, privacy-first approach. Your HealthKit data never has to pass through a third-party server, and most of these workflows run entirely on hardware you control using the open Model Context Protocol (MCP). Whether you are a quantified-self enthusiast, a biohacker, or just curious what an AI can tell you about your sleep, start with the guide that matches what you want to do.
Make export a background job that writes 190 metrics as clean JSON, then feed it live to your agent over a zero-dependency MCP server with 14 read-only tools.
Read the guide →How Apple's readiness score and Health Age work, what inputs they use, what Apple hasn't disclosed, and how MetricBridge shows the numbers behind the answer.
Read the guide →Apple's redesigned Health app adds summaries, a readiness score, and Health Age. MetricBridge shows the numbers behind every answer and adds an MCP bridge to your own agents.
Read the guide →Two on-device apps that answer Apple Health questions privately. One is free and stops at chat. The other exports 190 metrics to your own AI agents over MCP.
Read the guide →Claude's new Apple Health connector syncs your HealthKit data to Anthropic's cloud. What actually leaves your phone, and the on-device MCP alternative that keeps it local.
Read the guide →Which MCP server should you run to query Apple Health, Oura, Whoop, or Garmin data? Zero-dep vs DuckDB vs cloud-hosted, with the four criteria that matter and the one architecture to avoid.
Read the guide →Which health AI apps for iPhone keep your Apple Health data private? Six apps compared with data flow analysis, and the one that also bridges to Claude and Cursor via MCP.
Read the guide →ChatGPT Health sends your Apple Health data to OpenAI servers. On-device health AI answers the same questions on your phone. The data flow comparison, side by side.
Read the guide →One zero-dependency MCP server connects Apple Health to Claude Desktop, Cursor, ChatGPT, Windsurf, Codex CLI, opencode, and more. Real config for every client.
Read the guide →Does ChatGPT Health send your heart rate to OpenAI? A clear breakdown of what stays local vs what leaves your device when you use AI with your Apple Health data.
Read the guide →How to convert Apple Health export.xml to JSON. Streaming parsers for 1.8 GB files, Python scripts, browser converters, and the iOS app that skips XML entirely.
Read the guide →Four Apple Health MCP servers compared side-by-side: health-export-mcp (zero-dep), the-momentum (DuckDB), Health Auto Export (TCP), and neiltron. Setup times, tools, data models, and privacy.
Read the comparison →Apple Health export.xml has been broken since iOS 16: 1.7 GB files crashing Python, R, and Go parsers. Here's every DTD fix and the permanent workaround that skips XML entirely.
Read the guide →Two Apple Health MCP servers, two architectures. One requires Docker + Python + DuckDB + manual XML. The other is zero-dep Node.js with a companion iOS app. We compare setup, tools, data model, and privacy.
Read the comparison →On-device health AI keeps your data private. But without an MCP bridge, you can't feed that same data to Claude, Cursor, or any AI agent. Only one app ships both.
Read the guide →The exact webhook endpoint setup, the JSON payload your iPhone sends, and real ChatGPT prompts that turn your health data into actionable answers. Private: you control the endpoint.
Read the guide →Claude Code can write and run real analysis scripts against your Apple Health data (correlations, custom aggregates, charts) with nothing leaving your machine.
Read the guide →A private on-device health AI for iPhone that answers questions about your Apple Health data, without sending your health data to the cloud. Every answer shows its numbers.
Read the guide →Wire your Apple Health metrics into Cursor with the exact mcp.json config, MCP panel verification, and real queries you can run in Composer.
Read the guide →Turn HealthKit into clean, AI-ready JSON your agents can actually read, without manual CSV dumps or a cloud account.
Read the guide →Wire your Apple Health metrics into Claude with a zero-dependency MCP server so it can query your data with live tools.
Read the guide →Why the giant export.xml and flat CSVs choke LLMs, and how structured JSON with units and metadata wins.
Read the guide →Ask plain-language questions about your sleep, HRV, and steps. Get answers grounded in your real data.
Read the guide →MetricBridge exports 190 Apple Health metrics as clean JSON to the AI agent of your choice: read-only, local-first, no accounts. The MCP server is fully open source.
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