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

Annotations already declare readOnly, idempotent, non-destructive behavior, so the bar is lower. The description adds crucial context beyond annotations: these are reference values, not brand figures, and it discloses the available=false fallback when classification fails. It does not explain how the vertical is classified, a minor gap.

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?

Four tight sentences, front-loaded with what is returned, immediately followed by the critical caveat, then usage guidance and the failure-mode note. Every sentence 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?

No output schema exists, so the description must convey return semantics, which it does thoroughly: reference ranges, the not-brand-data warning, and the available=false failure mode. Complete for a zero-param read 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?

Zero parameters, so baseline is 4. The description appropriately spends its words on semantics of the return values (industry reference vs. brand figures) and the available=false sentinel.

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?

States a specific verb and resource: returns typical margin and ROAS ranges for the inferred commerce vertical. Distinguishes itself from sibling tools like get_margin_trend by making clear these are industry reference values, not the brand's own data.

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?

Gives explicit when-to-use guidance: 'Use only to say whether one of the brand's own numbers is normal for its category,' plus a strong prohibition against reporting these as the brand's figures. This routes the agent unambiguously.

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