Sleep MCP: Analyze Your Sleep Trends with Claude or ChatGPT

Sleep MCP connects your real sleep history to Claude or ChatGPT, so you can ask about trends, sleep debt, and how sleep is affecting your training — grounded in your actual data, not general advice. This guide covers Cora's real sleep tools, practical prompts, how sleep data informs training-load decisions through connected tools, and why read-only access is genuinely enough for this particular use case. Current status: live in production — see the full picture in our MCP overview.

Sleep tracking apps are good at showing you last night. They're a lot less good at answering the question that actually matters over time: is this a bad night, or is this a pattern? Answering that requires holding several weeks of data in your head at once and noticing a trend a single daily screen won't surface on its own — which is exactly the kind of reasoning a connected AI is well suited to.

Sleep is also the simplest of Cora's MCP data categories in one important way: it's entirely read-only in practice. There's no manual sleep logging tool to misuse, no write action that could go wrong. That makes it a good starting point for understanding what a scoped, read-only MCP connection actually feels like day to day.

What Cora's sleep-related MCP tools actually cover

Sleep sits within a broader group of read-only tools on Cora's MCP server, covering sleep, recovery, stats, and body data:

Get sleep. A window summary of your sleep — you can ask for the last 7, 30, 90, 180, or 365 days.
Get recovery. Your recovery score, HRV, and resting heart rate summarized over a chosen window — the metrics most directly influenced by how you slept.
Get stats. Summary statistics for any named tracked metric over a date range, sleep included.
Get daily scores. A merged, day-by-day table combining sleep, recovery, and activity for specific dates — useful for spotting how everything lines up on your worst and best days.
Get metric time series. Raw, per-day values for a single metric, which is what an AI needs to actually describe a trend line rather than just a summary number.

All five of these fall under read-only scopes (sleep:read, recovery:read, stats:read), which means the entire useful sleep workflow is covered by the default, look-but-don't-touch "Read my data" consent bundle. There's genuinely no write action to worry about here.

Trend analysis in Claude or ChatGPT

The most direct use case is asking about your own sleep pattern in plain language and getting an answer based on your actual numbers, not a general explanation of sleep science.

Real prompts mapped to real tools:

"How has my sleep duration trended over the last 90 days? Is it getting better or worse?" → pulls the sleep window summary and metric time series over that range.
"Was there a specific week where my sleep really tanked, and do you know why?" → cross-references daily scores to look for a pattern around that dip.
"Compare my average sleep on weeknights versus weekends for the last month." → uses the time series to break the pattern down by day type.
"My watch says my sleep score was low three nights this week — is that unusual for me, or pretty normal?" → compares recent nights against your longer-term baseline.

This is a meaningfully different experience than opening a sleep app and scrolling a chart yourself — you can ask a genuine follow-up question ("why do you think that happened") in the same breath, and the AI reasons over the same underlying data rather than you having to eyeball two separate weeks and compare them manually.

Sleep debt, in a conversation instead of a single number

Cora doesn't output one canonical "sleep debt" number through MCP today — that's worth saying plainly rather than implying a metric exists that doesn't. What it does have is your raw sleep history and summary stats over any window, which is actually the more useful input for an AI to reason with directly.

Example prompt:

"I'm aiming for 7.5 hours a night. Look at the last two weeks and tell me how far under that I've actually been, night by night, and what the cumulative shortfall looks like."

Because the AI can see the raw nightly values (not just an average), it can do that arithmetic transparently and explain its reasoning, rather than hand you an opaque score with no way to check the math. That transparency is one of the underrated advantages of chat-based analysis over a single dashboard metric — you can ask it to show its work.

How sleep data informs training-load decisions

This is where sleep MCP becomes genuinely more useful than sleep tracking in isolation. If you've also granted read access to your training data, the same AI conversation can combine both, the way a good coach naturally would.

Prompts that combine sleep and training:

"Look at my sleep for the last two weeks alongside my training log. Do my worst workouts line up with my worst sleep nights?"
"I've slept under 6 hours three nights running. Given my plan has a heavy squat day tomorrow, should I adjust it?"
"Is there a pattern between how I sleep the night before a hard session and how that session actually goes?"

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This kind of cross-domain reasoning — sleep informing a training decision in the same response — is exactly the design philosophy behind Cora's own recovery features and AI coaching: readiness isn't one metric in isolation, it's sleep, HRV, resting heart rate, and training load considered together. MCP just extends that same synthesis into whatever chat window you're already in. For the deeper mechanics, see our guide on how AI fitness coaching actually works, and our tools for a quick daily gut-check: the recovery calculator and training readiness calculator.

Live now — in production

Sleep MCP tools are live and functioning today on Cora's production server, at https://api.purplepill.ai/api/mcp/. Add it as a custom connector in Claude or ChatGPT and sign in with your existing Cora account. It's not yet listed in any AI connector directory, so add it manually for now — full setup at corahealth.app/mcp.

Privacy notes: why read-only access is genuinely enough here

Sleep is intimate data, and it's fair to want to understand the actual consent mechanics before connecting it to a third-party AI tool. A few specifics worth knowing:

Sleep has no write path. There's no MCP tool to manually log or alter sleep data — it's captured automatically from your wearable. That means granting sleep access can never let an AI change your sleep record, only read it.
Read access is the default, opt-out-only-by-not-approving bundle. "Read my data" is checked by default during setup and covers sleep, recovery, and stats together. You never have to separately opt into anything to get useful sleep analysis.
It's scoped per connected app, not global. If you connect Claude, that connection's access is independent of any other AI client you might connect later — each has its own grant, managed from that client's own connector settings.
Revocation is immediate. Remove the connector from Claude's or ChatGPT's own connector settings and access is gone on the next token refresh — there's no lingering access after you've pulled the plug. An in-app management screen inside Cora is coming soon.
Rate limits apply. Like every Cora MCP tool, sleep queries are subject to a per-connection rate limit (roughly 120 requests per minute), which is generous for normal conversational use but worth knowing if you're scripting repeated queries.

The short version: because sleep access is read-only by construction, the risk profile of connecting it is genuinely lower than granting write access to your training or nutrition data, where an AI could actually change something. If you're cautious about what to connect first, sleep and recovery read access is a reasonable, low-risk place to start.

Setting it up

Setup follows the same flow as every other Cora MCP connection: add Cora's live server URL (https://api.purplepill.ai/api/mcp/) in Claude or ChatGPT, sign in with your existing Cora account, and leave "Read my data" checked (it's the default) — that alone gives an AI everything it needs to analyze your sleep. Full setup steps live in our health MCP guide and on the Cora MCP page.

Key Takeaways

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Sleep MCP connects Claude or ChatGPT to your real sleep, recovery, and stats data through five read-only tools, letting an AI reason about trends instead of just showing you last night's number.
Sleep debt isn't a single output today, but an AI with access to raw nightly values and stats over any window can calculate and explain a cumulative shortfall transparently.
The most valuable workflow combines sleep with training data in one conversation — spotting whether poor sleep is lining up with poor sessions, and adjusting training load accordingly.
Sleep access is entirely read-only by design (no MCP tool edits sleep data), which makes it one of the lowest-risk consent bundles to grant if you're starting cautiously.
This is live in production today, with scoped, revocable, per-connection consent — connect it by adding the endpoint to Claude or ChatGPT as a custom connector.

Frequently Asked Questions

What is sleep MCP, and how does it work with Claude or ChatGPT?

Sleep MCP means connecting your real sleep data to an AI assistant like Claude or ChatGPT through the Model Context Protocol, so the AI can pull your actual sleep history — duration, trends over time, and how it relates to recovery — and reason about it in a normal conversation. With Cora's sleep tracking MCP tools, you can ask something like "how has my sleep trended over the last month" and get an answer grounded in your logged data, not a generic explanation of why sleep matters.

Can an AI tell me if I'm building up sleep debt?

Yes, in the sense that it can pull your sleep duration over any window you ask for (a week, a month, up to a year) and reason about the pattern — for example, flagging that you've averaged 6 hours against a stated 8-hour target for the last ten days. Cora's sleep tools don't output a single "sleep debt" number today, but an AI with access to your raw sleep history and a stats summary can calculate and explain that trend for you directly in the conversation.

How does sleep data inform training decisions through MCP?

Because the same connected AI can typically see both your sleep tools and your training tools (if you've granted read access to both), it can cross-reference them in one response — for example, noticing that your last three short-sleep nights lined up with your three worst training sessions, and recommending an easier week as a result. This cross-domain reasoning, sleep plus training in the same answer, is one of the more genuinely useful things a chat-based connection does that a single-metric dashboard doesn't.

Is read-only access enough for sleep data, or do I need to grant write access too?

For almost everyone, read-only access is all you need for sleep. Cora doesn't currently expose a way to manually log or edit sleep data through MCP — sleep is captured automatically from your wearable, not typed in — so the default "Read my data" consent bundle covers the entire useful surface area for sleep. You'd only need to grant additional access (like "Log & edit my data") if you also want the AI acting on your training or nutrition data in the same conversation.

What are the privacy implications of connecting sleep data to an AI assistant?

Sleep is sensitive, personal data, so it's worth understanding the actual consent model: Cora's MCP server uses OAuth 2.1 with explicit scoped bundles, and sleep read access falls under the default "Read my data" bundle you approve during setup. The AI only ever sees data for the account that signed in and approved the connection — there's no shared or aggregated access across users. This is a live production feature today; you can remove it anytime from Claude's or ChatGPT's own connector settings.

Can I use sleep MCP without also connecting my training or nutrition data?

Yes. Consent is granted per bundle, not all-or-nothing, so you could in principle grant only sleep-related read access if that's all you want an AI to see — though in practice, Cora's consent screen groups reads together under one "Read my data" bundle rather than offering sleep in isolation. If you want an AI to reason about sleep alongside training or recovery, which is where it's most useful, you'd want the same read bundle covering those areas too.