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

Annotations already declare readOnly, idempotent and non-destructive, so the safety profile is covered. The description still adds useful context: exactly what comes back (percentile, z-score, 3rd/15th/50th/85th/97th percentile values) and the notable constraint that no interpretation is included.

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?

Three compact sentences with the core purpose front-loaded and no wasted phrasing. The usegrowo.com/LMS reference is marginally promotional but does establish reference-table fidelity, so it earns its place.

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?

With no output schema, the description usefully enumerates the return payload and notes the absence of interpretation. It does not cover edge cases such as out-of-range ages or which reference standard applies at boundaries, but the schema handles the standard and corrected-age guidance.

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 100% and every parameter (sex, value, locale, metric, standard, ageMonths) is already well documented in the schema. The description adds no parameter-level detail beyond what the schema provides, so the baseline of 3 applies.

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?

The description states a specific computation (where one measurement sits on the WHO/CDC reference) and enumerates the outputs (percentile, z-score, percentile cut values). The phrase 'one measurement' implicitly distinguishes it from trend/velocity siblings, though no sibling is named explicitly.

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?

Usage is implied by scope rather than stated: 'one measurement' signals this is for a single point, not a series, and 'returns no interpretation' tells the model not to editorialize. There is no explicit when-to-use/when-not guidance or routing to alternatives such as zscore_percentile or bmi_percentile.

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