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google-health-mcp

health_get_weight_readings

Retrieve every weigh-in and body-fat reading Google recorded, including value, timestamp, and device, for detailed trend analysis.

Instructions

Get every weigh-in and body-fat reading, as Google recorded each one.

health_get_weight keeps one row a day; this returns every reading: its value in Google's own unit (weightGrams, percentage), when it was taken, and the device or app that recorded it.

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 weight_readings and body_fat_readings, each entry with reading_id, datetime (local), date, provider and record (the reading as Google sent it), and count, the number of readings of both kinds.

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. Addedv1.7.0

TDQS

A4.3/5.0
Behavior4/5

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 well: it discloses cache-vs-live behavior ('re-fetch this window from the API before reading the cache') and that values are returned in Google's native units (weightGrams, percentage). It does not cover auth requirements or rate limits, but for a read-only getter this is solid disclosure.

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 key differentiator in the first sentence, then structured Args/Returns sections. The Returns block restates what the output schema already provides, making it slightly redundant, but no sentence is wasteful for comprehension.

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?

All three parameters are explained, behavior (cache/live) is covered, and return shape is described even though an output schema exists. Complete enough for an agent to invoke correctly; the returns restatement is the only redundancy.

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 compensate, and it does: it documents accepted date formats ('YYYY-MM-DD', 'YYYY-MM', or '30d'), the default windows, and the live flag's effect. This adds real meaning beyond 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 every weigh-in and body-fat reading') and immediately distinguishes itself from the sibling health_get_weight by scope: 'health_get_weight keeps one row a day; this returns every reading.' An agent can route between the two without opening either schema.

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?

Names the alternative (health_get_weight) and the condition that selects the other tool (daily rows vs every reading), and documents default windows and the live/cache tradeoff. It does not explicitly say when to prefer this tool, but the contrast makes the use case clear.

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