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

Annotations already establish a safe, non-destructive, closed read. The description adds real value on top of that by disclosing the return shape: per-engine breakdown, competitor share-of-voice comparison, and a trend series with one point per scan, which tells the agent the granularity of the time series. It does not discuss limits or empty-data behavior, so not a 5.

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?

A single dense sentence that front-loads 'Headline AI visibility of a brand' and then enumerates the metrics. It is appropriately sized for a no-parameter-required read tool; the only soft spot is that the metric list reads like a return-value enumeration rather than an action statement.

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, the description carries the burden of explaining what comes back and does so adequately, listing the metric families and the trend granularity. It is thin on how defaults resolve when brand or dates are omitted, but that is largely covered by the schema's own defaults.

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 only obliquely touches parameters ('per engine', 'over the period') and adds no format, defaulting, or brand-resolution detail beyond what the schema already states for to/from/brand/engine.

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 names the resource precisely: headline AI visibility metrics for a brand (share of answers, share of voice, average position, sentiment, per engine, trend). That is specific enough to separate it from get_competitors, get_top_sources, and get_changes. It never states an explicit verb or names a sibling, so it stops short of a 5.

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, no prerequisites, and no mention of alternative tools such as search_responses or get_article_performance. The word 'Headline' faintly implies a summary-level call, but the agent is left to infer entirely when this tool is the right choice versus its many siblings.

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