Connect Apple Health to ChatGPT via Webhook — Your Data, Their Agent

ChatGPT is brilliant at reasoning over health data — parsing sleep patterns, spotting HRV trends, answering "how was my recovery this week?" — but it can't reach into Apple Health on its own. A webhook bridge gives it your structured data while keeping every transmission under your control. This guide shows you the exact endpoint to build, the JSON payload your iPhone sends, the real prompts that work, and where privacy holds — and where it doesn't.

By Health Export AI · Updated 3 August 2026 · ~10 min read

Quick answer: how the webhook path works

ChatGPT doesn't speak MCP natively, so it can't call tools on a local server the way Cursor or Claude Desktop can. Instead, data flows one direction: your iPhone → your endpoint → ChatGPT. Health Export AI on iOS reads Apple Health (read-only) and POSTs a structured JSON payload to a webhook URL you configure. You receive that payload on a server you own and feed it into ChatGPT however you like.

The endpoint is yours. The app never POSTs to a server run by the developer. For a comparison with the direct MCP path, see querying Apple Health with ChatGPT and Cursor.

What the webhook payload actually looks like

When Health Export AI sends data, the JSON is clean, typed, and ready for any LLM to consume. Here's a trimmed real example — the app exports up to 190 metrics, but the structure is the same for one or a hundred:

{
  "export": {
    "device": "iPhone16,2",
    "appVersion": "1.12.0",
    "exportedAt": "2026-08-03T09:14:22Z",
    "metricCount": 3,
    "dateRange": {
      "from": "2026-07-27T00:00:00Z",
      "to": "2026-08-03T09:14:22Z"
    }
  },
  "metrics": [
    {
      "type": "HKQuantityTypeIdentifierHeartRateVariabilitySDNN",
      "unit": "ms",
      "values": [
        { "date": "2026-08-03", "value": 62.1 },
        { "date": "2026-08-02", "value": 58.4 },
        { "date": "2026-07-28", "value": 71.2 }
      ],
      "source": "Apple Watch"
    },
    {
      "type": "HKQuantityTypeIdentifierRestingHeartRate",
      "unit": "count/min",
      "values": [
        { "date": "2026-08-03", "value": 54 },
        { "date": "2026-08-02", "value": 56 }
      ],
      "source": "Apple Watch"
    },
    {
      "type": "HKQuantityTypeIdentifierStepCount",
      "unit": "count",
      "values": [
        { "date": "2026-08-02", "value": 8432 },
        { "date": "2026-08-01", "value": 12109 },
        { "date": "2026-07-31", "value": 6718 }
      ],
      "source": "iPhone"
    }
  ]
}

Three things make this useful for an AI model:

• Every value has a unit. HRV is ms, resting heart rate is count/min. The model doesn't guess.
• Dates are ISO 8601. Models compute trends and deltas without parsing ambiguities.
• Source is preserved. Knowing whether a reading came from an Apple Watch or iPhone helps contextualise accuracy and gaps.

This structure is deliberate — Apple's own export.xml is verbose and drops context when pasted into a chat. For a full comparison, see Apple Health JSON vs CSV vs XML.

Three ways to set up the webhook

You have options, ranging from zero-code to fully automated. Pick the one that matches your setup.

No code Export to file, then paste

Open Health Export AI, tap Export, choose "Save file." The app writes .health-cache.json to your Files app. Drag it into ChatGPT and ask. Fastest path for a one-off analysis — nothing transits a network.

Try: "Here's my Apple Health JSON for the past week. What stands out about my recovery trend? Compare this week's HRV and resting heart rate to last week."

Automated A local Python endpoint

Stand up a tiny HTTP server on your Mac or Linux machine. The app POSTs automatically in the background. Here's a complete receiver in FastAPI:

# save as webhook_receive.py
# pip install fastapi uvicorn

from fastapi import FastAPI, Request, HTTPException
import json
from pathlib import Path

app = FastAPI()
DATA_DIR = Path.home() / "health-data" / "inbox"
DATA_DIR.mkdir(parents=True, exist_ok=True)

@app.post("/health-export")
async def receive_health_data(request: Request):
    token = request.headers.get("X-Webhook-Token")
    if token != "your-secret-here":
        raise HTTPException(status_code=403)

    body = await request.json()
    stamp = body["export"]["exportedAt"].replace(":", "-")
    path = DATA_DIR / f"health-{stamp}.json"
    path.write_text(json.dumps(body, indent=2))

    return {"ok": True, "saved": str(path)}

# Run: uvicorn webhook_receive:app --host 0.0.0.0 --port 8080

Point Health Export AI's webhook destination to http://your-mac.local:8080/health-export with the secret token. Every export lands as a dated JSON file. From there, open the latest in ChatGPT or wire it into a pipeline via the ChatGPT API.

No-code auto n8n or Home Assistant webhook

If you already run n8n or Home Assistant, create a webhook trigger node connected to a storage action — save to file, append to a Sheet, or forward to a custom GPT action via the ChatGPT API. Set the secret token in the app and in the webhook validator.

A real ChatGPT conversation walkthrough

Once the data is in ChatGPT's context, the interaction is fluid. Here's an exchange using the JSON payload shown above:

You: Here is my Apple Health data for this week as JSON. What stands out?

ChatGPT: A few things jump out:

1. **HRV is climbing.** SDNN went from 58.4 ms (Aug 2) to 62.1 ms (Aug 3),
   up from 71.2 ms (Jul 28). Rising HRV is a *positive* deviation from your
   personal baseline.

2. **Resting heart rate is low and steady** at 54–56 bpm.

3. **Step count is uneven** — 12,109 steps on Aug 1, down to 6,718 on Jul 31.

Want me to compare weekday vs weekend averages?

You: Yes — compare weekday vs weekend steps and HRV.

ChatGPT: • **Weekday avg:** ~9,800 steps · HRV ~64 ms
• **Weekend avg:** ~7,100 steps · HRV ~61 ms

Your activity drops ~28% on weekends. HRV follows by ~3 ms —
within normal day-to-day variation for your baseline.

The model reads the JSON, computes the comparisons, references your own baseline (not a population norm), and offers a follow-up — all without a spreadsheet. And it never offers medical conclusions — the language stays in "deviation from your personal baseline" territory.

If you're running a local MCP client instead of ChatGPT, the same prompts produce live, tool-driven answers. See connecting Apple Health to Claude via MCP.

The privacy distinction that matters

MCP path (Cursor, Claude Desktop, opencode): the data never leaves your machine. The MCP server reads your local JSON export over stdio. Zero network traffic.

Webhook path (ChatGPT, Gemini, Grok): the data travels from your iPhone to your endpoint, then you feed it into the model. That second hop — from your endpoint to ChatGPT's servers — is when the data leaves your control. The app never sends it to a third party, but once it's in ChatGPT, OpenAI sees it.

This isn't a flaw — it's a trade-off. Use MCP when your agent supports it (zero-egress queries). Use the webhook when you specifically want ChatGPT's reasoning. The app gives you the choice. More in Apple Health data privacy with AI agents.

When the webhook path makes sense

• You live in ChatGPT. If it's your daily driver for notes and summarisation, the webhook path keeps you in the interface you already use.
• You want scheduled summaries. Auto-export to your webhook receiver, and a cron job or n8n sends a weekly health prompt to the ChatGPT API.
• You need a custom GPT action. Wire the webhook into your GPT's action endpoint for on-demand health data.
• Zero-code is your preference. File export + drag into ChatGPT requires nothing more than the iOS app.

Frequently asked questions

Can ChatGPT query my Apple Health data directly?

No — ChatGPT doesn't speak MCP. Instead, Health Export AI pushes your Apple Health JSON to a webhook endpoint you control, and you feed that data into ChatGPT manually or through a pipeline. The data flows one direction — your phone to your endpoint to ChatGPT.

What does the webhook JSON payload look like?

The app POSTs an export metadata block (device, timestamp, metric count, date range) and a metrics array where each metric has a type, unit, values list, and source. Every value includes its date and numeric value — no nested XML. A full example is shown above.

Is my Apple Health data sent through a third-party server?

The app never sends your data to a server operated by the developer. Your webhook endpoint is entirely under your control. That said, once you paste or upload the data into ChatGPT, it reaches OpenAI's servers. Use the MCP path if you need zero-egress guarantees.

Do I need to write code to use the ChatGPT webhook?

No code for the simplest workflow: export to a file, drag it into ChatGPT. For automated webhook delivery, you'll need a small endpoint — either n8n or ~15 lines of Python (the FastAPI receiver above).

Can I use ChatGPT's API to receive the webhook directly?

Not directly — ChatGPT doesn't expose a public HTTP endpoint that receives arbitrary JSON. But the ChatGPT API does: write a small relay (n8n or a Cloudflare Worker) that receives the webhook and forwards it via the OpenAI API. This requires a ChatGPT API key.

Related reading

• → Query your Apple Health data with ChatGPT and Cursor (the two-path overview)
• → Connect Apple Health to Claude via MCP — step-by-step
• → Export Apple Health data to JSON for AI (the pillar guide)
• → Apple Health JSON vs CSV vs XML — which format AI agents actually want
• → Apple Health data privacy with AI agents — what stays local, what leaves
• → health-export-mcp — the zero-dependency MCP server on GitHub