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Intraday samples

get_intraday

Read-onlyIdempotent

Fetches intraday HRV samples from your Apple Health data, returning hourly averages or raw readings to track heart rate variability changes throughout the day.

Instructions

Samples WITHIN a day, for the series that record several per day (HRV).

granularity:

"hourly" (default) — one row per hour that HAS samples (UTC hour boundaries, shown in local time), the mean SDNN of that hour and its sample_count. Right for "how did my HRV move through the day".
"raw" — one row per sample, newest first.

The Watch measures HRV a handful of times a day, not continuously (more during a Breathe session), so most days have about 8-14 rows either way. limit counts ROWS, not days: the default 168 is roughly two to three weeks, not one. How far back the history goes depends on what this phone uploaded, not on a fixed window — read coverage.oldest_available and coverage.more_available. For one number per day use get_metric. Reads YOUR data only: HRV is never shared between partners.

rows is a table in columns order (local_time, sdnn_ms, plus sample_count for hourly). Carries a coverage block: quote coverage.days_covered (distinct days, not the row count) beside any number.

Input Schema

TableJSON Schema

Name	Required	Default
`fresh`	No
`limit`	No
`series`	No	hrv_sdnn
`granularity`	No	hourly

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.9.1Oct 3, 2026

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds genuinely non-obvious behavior: HRV is measured sporadically (8-14 rows/day), history length depends on what the phone uploaded rather than a fixed window, and the coverage block must be read for oldest_available/more_available. The only gap is the unexplained `fresh` parameter's effect.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense but front-loaded: the core scope lands in the first sentence, then granularity options, then caveats. Every sentence carries information, though the granularity bullet block makes it longer than strictly needed for a read tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, yet the description still usefully explains row shape, column order, and the coverage block — appropriate for a data-returning tool. It is nearly complete for an agent's needs, missing only an explanation of what `fresh` does and what it changes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must carry the load, and it does well for three of four parameters: granularity modes are fully defined, limit semantics (rows, default 168 ≈ 2-3 weeks) are clarified, and series is implied as HRV SDNN. `fresh` is never mentioned, leaving one parameter undocumented in both schema and description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (samples) and a precise scope: within-day samples for series that record multiple times per day (HRV). It explicitly distinguishes itself from get_metric ('For one number per day use get_metric'), so an agent can route between the two without reading schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use guidance for each granularity mode ('hourly' for how HRV moved through the day, 'raw' for per-sample rows) and names the alternative tool for daily aggregation. It also warns that limit counts rows not days, which prevents a common misuse.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.