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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by framing the operation as reading 'recorded answers' and returning measurements, which implies a read-only, side-effect-free operation. It also discloses what the grouping options produce (trend versus bucket comparisons), exceeding the minimal safety disclosure expected.

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, each earning its place: the first lists the metrics, the second explains grouping choices, and the third gives dashboard context. The description is front-loaded with the core function and contains no filler.

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 two-parameter read tool with no output schema, the description explains the return concept well by naming the metrics and grouping behavior. It doesn't specify the exact response shape or edge cases, but with simple parameters and no nested objects, that is a minor gap.

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%, so the baseline is 3. The description adds meaning by tying the dimension parameter to analytical intent ('find where a brand is weak') and explicitly mapping day to trend and the other dimensions to comparisons, going slightly beyond the schema's terse enum descriptions.

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 uses a specific verb 'read' and resource 'visibility' while enumerating exact metrics: visibility, share of voice, mean position, and competitor ranking. It clearly distinguishes this tool from sibling tools like read_document and discover_competitors by focusing on the account's own answer-engine measurements.

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?

The description provides clear context by stating this is 'the data behind the dashboard's Answer Engine Insights' and gives explicit guidance on when to use each grouping: day for trends, and model/region/persona/prompt/topic for finding weak spots. It doesn't name when-not-to-use alternatives, but the context is strong and the sibling list shows no overlapping analytics tools.

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