VO₂ max

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety is covered. The description adds rich behavioral detail: it explains the coverage block fields (days_covered, span_days, window_satisfied, more_available), warns about limit-shaped windows being misreported as full history, and clarifies that the data is decrypted. This goes well beyond the annotations.

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

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

The description is well-structured in three paragraphs: core purpose and reference ranges, coverage block semantics, and the more_available warning. It front-loads the key information and each sentence adds value. It is longer than average but the complexity of the coverage behavior justifies the length.

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

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

Given there is an output schema (not shown) and annotations cover safety, the description covers the essential behavioral context: sparsity, limit behavior, owner filtering, coverage interpretation, and the warning about limit-shaped windows. The only gap is the 'fresh' parameter, which is not explained. Overall it is highly complete for a get-metric tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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. It explicitly explains owner ('filter by person') and limit (sparsity recommendation, effects on coverage), but does not address the 'fresh' parameter at all. With 3 parameters and no schema descriptions, missing one param's semantics is a notable gap, though it partially compensates for two.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool decrypts recent VO2Max samples from Apple Watch cardiorespiratory fitness, provides units and reference ranges, and notes the sparsity. It is distinct from sibling get_* tools by explicitly describing the unique coverage block and limit semantics, so an agent can differentiate it from get_resting_hr or get_hrv.

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

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

Provides concrete usage guidance: recommends a limit of 30 given sparse data, explains how to filter by owner, and instructs how to interpret coverage fields (e.g., window_satisfied false means shorter history, more_available true means older data exists). It doesn't explicitly name alternatives or exclusions, but the metric-specific naming and detailed context make the intended use clear.

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