google-health-mcp

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health_get_core_temperature

Retrieve manually logged core body temperature readings in Celsius to track fever or body-temperature changes across a date range.

Instructions

Get manually-logged core (body) temperature readings (degrees Celsius).

These are absolute body temperatures the user enters by hand - a thermometer reading logged in an app - and are the right source for fever / body-temperature questions. They are NOT the device-derived nightly skin-temperature variation from health_get_skin_temperature. A single day can hold several readings (each timestamped), useful for tracking a fever over time.

Args: start_date: Start date as "YYYY-MM-DD", "YYYY-MM", or "30d". Default: last 30 days. end_date: End date as "YYYY-MM-DD". Default: today. live: If true, re-fetch this window from the API before reading the cache.

Returns one entry per logged reading with datetime (YYYY-MM-DDThh:mm:ss), temp_celsius, measurement_location (where it was taken, e.g. "MOUTH" or "EAR", as logged), reading_id and data_source. A reading whose value is changed in the app is kept beside its earlier value rather than replacing it, so two readings logged at the same datetime may be one reading, edited.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`live`	No
`end_date`	No
`start_date`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 20, 2026

TDQS

A5/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden and does so thoroughly. It discloses that readings are manually logged, may be multiple per day, include timestamps, can be returned with edited duplicates kept beside earlier values, and that live=true refetches from the API before reading cache.

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

Conciseness5/5

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

The description is appropriately sized and front-loaded: purpose and distinction first, then Args, then Returns. Every sentence adds useful meaning, including the important note about edited readings appearing as multiple entries at the same datetime.

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

Completeness5/5

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

Given the lack of annotations, zero schema description coverage, and a rich output schema, the description is complete enough for correct invocation and interpretation. It covers data provenance, sibling distinction, parameter formats and defaults, cache/live behavior, and return-field semantics.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must document parameters itself, and it does: start_date accepts YYYY-MM-DD, YYYY-MM, or 30d and defaults to last 30 days; end_date accepts YYYY-MM-DD and defaults to today; live controls API re-fetch before cache read.

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?

The description states a specific verb and resource: getting manually-logged core body temperature readings in Celsius. It explicitly distinguishes this from the device-derived skin-temperature variation from health_get_skin_temperature, so an agent can select it without confusion.

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?

It gives clear when-to-use guidance: these readings are the right source for fever and body-temperature questions. It also names the alternative source to avoid, health_get_skin_temperature, and explains the difference between manual core temperature and device-derived skin temperature.

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