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 over 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.
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 — and get answers grounded in your real data.
Read the guide →Health Export AI 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.
Get Health Export AI View on GitHub