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davidmosiah

Google Health MCP

by davidmosiah

Google Health Wellness Context

google_health_wellness_context
Read-onlyIdempotent

Normalize Google Health activity and sleep data into a standardized wellness context for recommendation engines.

Instructions

Normalize Google Health activity/sleep context into the shared wellness_context shape for recommendation engines.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLookback window for normalized Google Health wellness context.
timezoneNoIANA timezone used only for display, e.g. America/New_York.UTC
sorenessNo
injury_flagsNo
notesNo
response_formatNomarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYes
generated_atYes
recent_training_loadYes
sorenessYes
injury_flagsYes
notesYes
Behavior3/5

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint, providing a strong safety profile. The description adds minimal behavioral context (normalization step), but does not contradict annotations. No additional traits like prerequisites or data freshness requirements are disclosed.

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

Conciseness3/5

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

The description is a single sentence of 17 words, which is concise but lacks structure for a tool with six parameters. The sentence is front-loaded with the core purpose, but the brevity may hinder completeness. A slightly longer description with parameter context would improve usability without losing conciseness.

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

Completeness3/5

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

The presence of an output schema reduces the need to explain return values, but the description omits details about the normalization process, parameter dependencies, or typical usage scenarios. With moderate complexity (6 parameters, low schema coverage), the description is incomplete for an agent to confidently invoke the tool without inferring from names.

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?

Schema description coverage is low (33%), with only 'days' and 'timezone' having descriptions. The tool description does not explain any parameters, leaving the agent without added semantic guidance for the four undocumented parameters (soreness, injury_flags, notes, response_format). The description fails to compensate for the schema's gaps.

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 the action ('normalize'), the resource ('Google Health activity/sleep context'), and the target output ('shared wellness_context shape for recommendation engines'). It distinguishes from sibling tools like google_health_daily_summary or google_health_list_data_points by indicating normalization as a specific transformation step.

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

Usage Guidelines3/5

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

The description implies usage when wellness context is needed for recommendation engines, but provides no explicit guidance on when not to use this tool or what alternatives exist among the many sibling tools. Context from sibling names (e.g., raw data vs. rollups) offers implicit differentiation, but the description itself lacks direct usage boundaries.

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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