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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the description's added value is the row semantics: one row per tracked brand, zero-filled visibility/share-of-voice for brands with no mentions, and null sentiment/position. This is real behavioral detail beyond the annotations, though pagination is only gestured at ('Paginated') without cursor/page mechanics.

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 tight sentences: metric set first, then row semantics, then pagination. No filler, and the most decision-relevant information is front-loaded.

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?

For a 13-parameter read-only aggregate tool with a fully documented schema and no output schema, the description covers the metric set, per-row behavior, and null/zero handling well. The main gap is filter-interaction guidance (how segments/tags/countries narrow the aggregates) and concrete pagination behavior.

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 every one of the 13 parameters is already documented in the schema; the description adds only the vague notion of a 'filter window'. It does not clarify how filters like segments, tags, or monitors combine, so it is baseline-level for a high-coverage schema.

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 (per-brand aggregates) and resource (brands), and enumerates the metrics returned: visibility, share-of-voice, sentiment, position. It implicitly separates itself from the time-series and monitor/prompt/source analytics siblings via the 'per-brand' framing, but never names an alternative, so an agent must infer the distinction.

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?

There is no when-to-use guidance and no mention of alternatives despite many adjacent analytics siblings (list-monitor-analytics-tool, list-prompt-analytics-tool, get-*-time-series-tool). The reader must infer that aggregate-per-brand is wanted rather than a time series or a different entity dimension.

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