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

With no annotations provided, the description carries the transparency burden and does well: it discloses the 30-minute refresh cycle, 48-hour lookback window, precomputed scores, and that it is free with no auth. It omits minor operational details like limits or pagination, so a 4 is appropriate.

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?

The description is front-loaded with purpose and packs useful facts (frequency, window, auth, example use cases) into four sentences. The opening phrase is somewhat colloquial, but no sentence is superfluous.

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-only data query with no output schema, the description gives an agent enough context to invoke it: what data is returned, how fresh it is, and when to use it. Exact output shape is not specified, but the described fields (brand names and scores) are sufficient for a simple 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%, and both parameters (limit and category) already have semantic descriptions in the input schema. The tool description adds no parameter-level detail beyond mentioning 'category X', so it stays at the baseline 3.

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 states a clear output: a record of brands ChatGPT names in response to buyer questions, paired with each brand's AI visibility score, over the last 48 hours. This identifies the specific resource and differentiates it from sibling tools such as aeo_score or brand_profile by describing the data source and update mechanism.

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?

Provides explicit example questions ('which brands does AI recommend in category X', 'is an AI visibility score related to being recommended') that tell an agent when to choose this tool. It does not mention alternatives or exclusions, so it stops short of a 5.

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