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fitbit_get_cardio_fitness

Retrieve your Fitbit Cardio Fitness Score (VO2 Max estimate) from cached data or live API. Get weekly VO2 Max ranges (mL/kg/min) for specified dates.

Instructions

Get Cardio Fitness Score (VO2 Max estimate).

Fitbit estimates VO2 Max from resting HR, HR during walks/runs, and demographics. Updates roughly weekly. Returns from cache by default, auto-syncing if stale.

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, fetch directly from Fitbit API instead of cache.

Returns entries with vo2_max_low and vo2_max_high (mL/kg/min). Fitbit reports as a range (e.g. 39-43); when a single value is given, low and high are equal. Higher = better cardiorespiratory fitness.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
liveNo
end_dateNo
start_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: the source of the estimate, weekly update frequency, default cache behavior with auto-sync, and the live parameter for direct API fetch. It also explains the return format (vo2_max_low/high range) and semantics (equal when single value, higher is better).

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

Conciseness5/5

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

The description is well-structured with a clear purpose statement, brief explanatory notes, and an organized Args/Returns format. Every sentence adds value, and the length is appropriate for the complexity of the tool.

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 the tool's simplicity and the presence of an output schema (noted but not detailed here), the description covers inputs, outputs, cache behavior, and update frequency. It provides enough context for an agent to select and invoke the tool correctly without needing additional information.

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?

Despite 0% schema description coverage, the description explicitly explains all three parameters: start_date formats ('YYYY-MM-DD', 'YYYY-MM', or '30d') and default, end_date format and default, and the live boolean's meaning. This fully compensates for the schema's lack of descriptions.

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 'Get Cardio Fitness Score (VO2 Max estimate)' with a specific verb and resource, and distinguishes from sibling tools by focusing on cardio fitness rather than other metrics. It further explains what the score estimates (from resting HR, HR during walks/runs, demographics), making the tool's purpose unambiguous.

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?

The description provides clear context for usage: updates roughly weekly, returns from cache by default, and auto-syncs if stale, which implies when to use the 'live' parameter. It does not explicitly mention alternatives or when-not conditions, but the context is enough to guide the agent on typical use cases.

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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