Skip to main content
Glama
partymola

google-health-mcp

health_get_hrv

Retrieve nightly heart rate variability (HRV) readings from a local cache or live API to track recovery and parasympathetic trends.

Instructions

Get nightly HRV (heart rate variability) data.

Returns data from the local cache by default. Use live=True to fetch from the API. Run health_sync first to populate the cache.

HRV data is sparse: only nights with on-wrist sleep tracking produce readings.

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 night with daily_rmssd and deep_rmssd (ms), entropy, non_rem_hr (non-REM heart rate, bpm) and data_source (the device and platform Google says recorded it). RMSSD = root mean square of successive RR interval differences. Higher values generally indicate better recovery and parasympathetic activity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
liveNo
end_dateNo
start_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does well: it discloses the cache-vs-live tradeoff, the sync prerequisite, and the sparsity constraint ('only nights with on-wrist sleep tracking produce readings'). This prevents the agent from misinterpreting empty results. It omits auth/permission requirements, keeping it from a 5.

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?

Front-loaded with the core purpose, then behavior, then Args. Size is reasonable, though the RMSSD definition and return-field enumeration could arguably live closer to the output schema. Every sentence serves a purpose, so no serious waste.

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 a 3-param tool with 0% schema coverage, no annotations, and an output schema, the description fills the gaps: parameter formats, cache/live behavior, prerequisites, and data sparsity. An agent has everything needed to call it correctly and interpret empty results.

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 coverage is 0%, so the description must compensate fully — and it does. It documents all three parameters: accepted date formats ('YYYY-MM-DD', 'YYYY-MM', '30d') with defaults for start_date/end_date, and the re-fetch semantics of live. This adds substantially more than the bare schema types.

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 and resource: 'Get nightly HRV (heart rate variability) data.' The scope ('nightly') and the explicit RMSSD explanation clearly distinguish it from siblings like health_get_heart_rate, which covers a different metric.

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

Usage Guidelines4/5

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

Gives clear operational context: data comes from the local cache by default, live=True re-fetches from the API, and health_sync must run first to populate the cache. It stops short of explicitly naming which sibling to use for the raw heart-rate metric, so it is clear but not fully routing.

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