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partymola

google-health-mcp

by partymola
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

health_trends

Compute period-wise averages and totals from cached health data, optionally comparing two windows. Spot trends in activity, sleep, heart rate, weight, SpO2, HRV, and more.

Instructions

Analyse trends in cached health data.

Computes averages and totals over time from the local cache, auto-syncing if stale.

Args: data_type: What to analyse. Options: "heart_rate", "activity", "exercises", "sleep", "weight", "spo2", "hrv", "azm", "breathing_rate", "skin_temperature", "core_temperature", "cardio_fitness", "food_log". Default: "activity". period: Aggregation period. Options: "weekly", "monthly", "quarterly". Default: "monthly". start_date: Start date as "YYYY-MM-DD" or "365d". Default: last 12 months. end_date: End date as "YYYY-MM-DD". Default: today. compare: Compare two periods. Format: "last_30d vs previous_30d", "2026-03 vs 2026-02", "2026-Q1 vs 2025-Q4". When set, period/start_date/end_date are ignored.

Returns aggregated averages per period. For activity: steps, distance, active minutes. For exercises: sessions, duration, calories. For sleep: duration, efficiency, stage breakdown. For heart_rate: resting HR min/avg/max. For weight: weight, fat%, BMI. For hrv: daily and deep RMSSD.

Two data types report their history under two definitions, each with the count of readings behind it - never merge them into one series. For spo2, min_spo2/max_spo2 are observed nightly extremes, while avg_nightly_ci_low/avg_nightly_ci_high average the nightly bounds of a confidence interval on each night's own average. For cardio_fitness, avg_vo2_max_low/high average a reported band while avg_vo2_max averages a single value.

Those per-definition fields and their counts appear in the period form only. compare= reports spo2 as avg_spo2 alone, which is deliberate and not a gap: that column means the same thing under both definitions, so it is the one figure two windows either side of the switchover can be compared on. Ask for the period form when you need to know which definition is behind a number. cardio_fitness has no such neutral column, so compare= carries both definitions there. Not for raw data - use health_get_* tools instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNomonthly
compareNo
end_dateNo
data_typeNoactivity
start_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.4/5.0
Behavior4/5

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

It discloses auto-syncing if stale, the behavior of compare (ignoring period/start/end), and details about how spo2 and cardio_fitness are handled in output. However, the verbose and repetitive explanation makes the transparency less crisp than it could be, and it doesn't mention error handling or side effects beyond that.

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

Conciseness2/5

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

The description is excessively long and repetitive, with near-duplicate paragraphs about spo2/cardio_fitness formatting. It could be condensed significantly while preserving key information. The structure mixes args, returns, and deep nuances in a convoluted flow.

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?

Given the complexity and lack of annotations, the description covers essential aspects: purpose, parameters, output details, and alternates. It includes auto-sync behavior and the raw-data alternative. Slightly incomplete regarding potential edge cases or error conditions, but the main context is well addressed.

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?

The schema has zero description coverage, so the description fully compensates by explaining every parameter: data_type with options, period with options, start_date, end_date, and compare including its override behavior. All parameters are covered.

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 clearly states it analyzes trends in cached health data and computes averages/totals over time. It also distinguishes from siblings by explicitly saying 'Not for raw data - use health_get_* tools instead.'

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?

The description explicitly directs when not to use it (for raw data) and indicates when to use it (for trend analysis). It also explains the parameter behavior, such as compare overriding other date/period parameters.

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

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