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

Annotations cover the read-only/idempotent/ non-destructive safety profile, and the description adds meaningful behavior beyond that: the single-brand line reads 100, and notably that the top_n cap never affects the denominator. That denominator caveat is a genuine behavioral disclosure not present in 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?

Three sentences, front-loaded with the metric definition before the scoping rules. Dense but every clause carries information; the parenthetical default/max is compact.

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 but 100% parameter description coverage and read-only annotations, the description is nearly complete for a read analytics tool. It covers the non-obvious scope/cap logic; only the temporal interval semantics are left entirely to the schema.

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 coverage is 100%, so baseline is 3, but the description adds semantics the schema lacks: what a brand_id-scoped result reads (100 in buckets with mentions) and the default/max/cap-doesn't-affect-denominator behavior of top_n. This meaningfully deepens interpretation of the key scope parameters.

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 specific metric computed: each brand's mentions as a percentage of scoped brands' mentions, per bucket. A specific verb+resource+computation is defined, though it does not explicitly differentiate itself from metric siblings like get-visibility-time-series-tool or get-sentiment-time-series-tool.

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?

It explains how scoping choices (brand_id, segments, otherwise top_n) change the result, which implies usage, but never states when to choose this tool over its time-series siblings. The guidance is about parameter effects rather than tool selection.

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