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

With no annotations provided, the description carries the full transparency burden. It discloses the cost (`$0.09 via x402`), that the tool polls a live model ('ask a current model'), and the output shape (a shortlist ranked by mention share). It does not cover edge cases like rate limits or errors, but the essential behaviors are plainly stated.

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 very tight: three short sentences that lead with the core function, add the key differentiator, and end with the target audience. There is no filler content, and each line carries meaningful information for selecting the tool.

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 tool with one required parameter, no output schema, and no annotations, the description provides enough to know the outcome and the payment needed. It tells an agent that the response is 'ranked shortlist of brands ... by mention share' and highlights the use-case context. A fully exhaustive explanation of the market parameter or the x402 payment mechanism would improve completeness, but the description is sufficient for a typical invocation.

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?

The schema already documents `category` and `market`, leaving only `x_payment` undocmented. The description reinforces that `category` is open-ended ('ANY category') and hints at payment semantics through the `$0.09 via x402` sentence, but it does not elaborate on `market` or `x_payment` beyond the schema. With a coverage of 67%, it provides modest additional value but does not fully compensate for the missing `x_payment` description.

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 clearly states the tool's function: it runs a live AI category ranking, asks a current model for recommendations, and returns a ranked shortlist of mentioned brands by share of mentions. It explicitly distinguishes itself as 'the inverse of a brand check', which sets it apart from the sibling tool `brand_ai_visibility_check` and gives the agent a precise understanding of what it does.

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 names the intended user contexts (competitive-intel, market-research, GEO and sales agents) and frames the tool as the inverse of a brand check, implying when to use it for a full landscape versus checking a single brand. However, it does not give an explicit 'when not to use' or name an alternative directly, so it falls slightly short of a perfect 5.

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