Google Health MCP

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Google Health Physical-Time Rollup

google_health_rollup

Read-onlyIdempotent

Retrieve health metrics for a chosen data type and aggregate them into fixed-time windows over a date range, returning summarized values to track changes across intervals.

Instructions

Aggregate a data type over physical time intervals using Google Health rollUp.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`end_time`	Yes	ISO 8601 date-time with timezone, e.g. 2026-05-01T00:00:00Z
`data_type`	No	Google Health data type in kebab case. Supported slugs (call google_health_list_data_types for units and which verbs each supports): active-energy-burned, active-minutes, active-zone-minutes, activity-level, altitude, blood-glucose, body-fat, calories-in-heart-rate-zone, core-body-temperature, daily-heart-rate-variability, daily-heart-rate-zones, daily-oxygen-saturation, daily-respiratory-rate, daily-resting-heart-rate, daily-sleep-temperature-derivations, daily-vo2-max, distance, electrocardiogram, exercise, floors, food, food-measurement-unit, heart-rate, heart-rate-variability, height, hydration-log, irregular-rhythm-notification, nutrition-log, oxygen-saturation, respiratory-rate-sleep-summary, run-vo2-max, sedentary-period, sleep, steps, swim-lengths-data, time-in-heart-rate-zone, total-calories, vo2-max, weight. Other valid v4 kebab-case slugs are also accepted.	steps
`page_size`	No
`page_token`	No
`start_time`	Yes	ISO 8601 date-time with timezone, e.g. 2026-05-01T00:00:00Z
`window_size`	No	Duration in protobuf seconds format, e.g. 3600s.	3600s
`privacy_mode`	No	Optional per-call privacy override. Defaults to GOOGLE_HEALTH_PRIVACY_MODE or structured. raw returns upstream Google Health JSON.
`response_format`	No		markdown
`data_source_family`	No
`explicit_user_intent`	No	Required true when privacy_mode=raw (agent escalation of redaction).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes
`endpoint`	Yes
`privacy_mode`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedv0.7.7Aug 29, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

changedInput schema / properties / end_time / pattern

Previous value: -"^(?:(?:\\d\\d[2468][048]|\\d\\d[13579][26]|\\d\\d0[48]|[02468][048]00|[13579][26]00)-02-29|\\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\\d|30)|(?:02)-(?:0[1-9]|1\\d|2[0-8])))T(?:(?:[01]\\d|2[0-3]):[0-5]\\d(?::[0-5]\\d(?:\\.\\d+)?)?(?:Z|([+-](?:[01]\\d|2[0-3]):[0-5]\\d)))$"New value: +"^(?:(?:\\d\\d[2468][048]|\\d\\d[13579][26]|\\d\\d0[48]|[02468][048]00|[13579][26]00)-02-29|\\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\\d|30)|(?:02)-(?:0[1-9]|1\\d|2[0-8])))T(?:(?:[01]\\d|2[0-3]):[0-5]\\d:[0-5]\\d(?:\\.\\d+)?(?:Z|([+-](?:[01]\\d|2[0-3]):[0-5]\\d)))$"

changedInput schema / properties / start_time / pattern

Previous value: -"^(?:(?:\\d\\d[2468][048]|\\d\\d[13579][26]|\\d\\d0[48]|[02468][048]00|[13579][26]00)-02-29|\\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\\d|30)|(?:02)-(?:0[1-9]|1\\d|2[0-8])))T(?:(?:[01]\\d|2[0-3]):[0-5]\\d(?::[0-5]\\d(?:\\.\\d+)?)?(?:Z|([+-](?:[01]\\d|2[0-3]):[0-5]\\d)))$"New value: +"^(?:(?:\\d\\d[2468][048]|\\d\\d[13579][26]|\\d\\d0[48]|[02468][048]00|[13579][26]00)-02-29|\\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\\d|30)|(?:02)-(?:0[1-9]|1\\d|2[0-8])))T(?:(?:[01]\\d|2[0-3]):[0-5]\\d:[0-5]\\d(?:\\.\\d+)?(?:Z|([+-](?:[01]\\d|2[0-3]):[0-5]\\d)))$"

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

Addedv0.7.3Aug 4, 2026
Removedv0.5.3Jul 17, 2026

Changed1 schema field changedv0.5.1Jun 29, 2026

changedInput schema / properties / data_type / description

Previous value: -"Google Health data type in kebab case, e.g. steps, sleep, heart-rate, daily-resting-heart-rate."New value: +"Google Health data type in kebab case. Supported slugs (call google_health_list_data_types for units and which verbs each supports): active-energy-burned, active-minutes, active-zone-minutes, activity-level, altitude, blood-glucose, body-fat, calories-in-heart-rate-zone, core-body-temperature, daily-heart-rate-variability, daily-heart-rate-zones, daily-oxygen-saturation, daily-respiratory-rate, daily-resting-heart-rate, daily-sleep-temperature-derivations, daily-vo2-max, distance, electrocardiogram, exercise, floors, food, food-measurement-unit, heart-rate, heart-rate-variability, height, hydration-log, irregular-rhythm-notification, nutrition-log, oxygen-saturation, respiratory-rate-sleep-summary, run-vo2-max, sedentary-period, sleep, steps, swim-lengths-data, time-in-heart-rate-zone, total-calories, vo2-max, weight. Other valid v4 kebab-case slugs are also accepted."

First observedv0.1.3May 7, 2026

TDQS

C2.9/5.0

Behavior3/5

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

The annotations already establish that this is read-only, idempotent, and non-destructive, so the description does not need to cover side effects. It adds the basic aggregation-over-physical-time behavior, which is useful context, but it leaves out pagination, privacy-mode behavior, and windowing semantics that would help the agent understand the call's full behavior.

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?

The description is one tight sentence with the core verb front-loaded, which is easy to scan. 'using Google Health rollUp' is slightly redundant given the tool name, but there is no fluff or unnecessary elaboration.

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

Completeness2/5

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

This is a 10-parameter aggregation tool with a rich output schema and many siblings, but the description gives no operational context about privacy_mode=raw, data_source_family, pagination, or how it differs from google_health_daily_rollup. The output schema reduces the need to document return values, but the surrounding behavior is still underdescribed.

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

Parameters2/5

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

With 10 parameters and only 60% schema description coverage, the description should compensate for undocumented parameters such as page_size, page_token, response_format, and data_source_family. The description only vaguely implies data_type and time-interval concepts, adding little meaning beyond what the input schema already documents.

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

Purpose4/5

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

The description states a specific action ('Aggregate') and resource ('a data type over physical time intervals'), which gives the agent a clear sense of what the tool does. The phrase 'physical time intervals' helps distinguish it from daily-rollup siblings, though it does not name an alternative explicitly. It is not a tautology: it adds the concept of aggregation over physical time beyond the title.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus google_health_daily_rollup, google_health_list_data_points, or the summary tools. The description implies a use case but gives no exclusions, prerequisites, or alternative routing, leaving the agent to infer when this is the right choice.

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