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

Annotations already declare readOnlyHint, idempotentHint and non-destructive, so safety is covered. The description adds genuine behavioral context the annotations don't: the computed metric is a mention share per brand and the output is bucketed by interval, plus the default/max on top_n. It stops short of describing return shape or empty-result behavior.

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?

Two dense sentences with no filler, front-loading the core metric before the scoping rules. Every clause carries information about either computation or parameter behavior.

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 11 params at full schema coverage and capped top_n, the structured data carries most detail. The description covers the core metric and scoping logic, and no output schema exists to defer to, but it omits any indication of the returned series format. Acceptable but not exhaustive for an analytics endpoint.

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 the baseline is 3. The description earns above that by explaining the interaction between brand_id, segments and top_n – a mutually-exclusive scoping priority that the individual schema descriptions do not express on their own.

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 – the share of analyzed responses mentioning each tracked brand, bucketed by interval. However, despite close siblings such as get-share-of-voice-time-series-tool and get-brand-mention-tool, it offers no explicit differentiation, so an agent cannot cleanly separate this from its near-neighbors.

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 works (brand_id for one line, segments for segment brands, otherwise top_n), which implies intended usage, but never states when to choose this tool over the sibling time-series analytics tools. Usage is inferable rather than explicit.

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