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

Adds substantial behavior beyond the annotations: it discloses that successfulModelCount is the denominator (raw counts, not percentages or rankings), that entities[0] is always the target brand with competitors in submitted order, that a failed model reports status error with mentioned/cited null (not false) because it was never checked, and that it requires a paid plan and charges per-request.

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?

Front-loads the core 'what it does' in the first sentence, then layers in scope limits, output semantics, and cost. It is dense and long, but virtually every clause carries distinct information an agent needs, so only minor trimming would be possible.

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 full burden and delivers: it explains mentionedByCount/citedByCount denominators, entities[0] ordering, and error/null handling per failed model. Combined with the cost and plan disclosures, an agent has everything needed to call and interpret it correctly.

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 schema already documents brand, competitors, models, prompt, projectId, and the optional web/locale params. The description's added detail (entities ordering, persona nature of models, per-answer comparison) mostly pertains to output semantics rather than the parameters themselves, so baseline 3 is appropriate.

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+resource: runs ONE prompt through the explore_ai_prompt pipeline and checks a brand plus competitors against the same per-model answers. It explicitly distinguishes its scope ('what one prompt's observed answers show for the named entities') from a comprehensive cross-prompt visibility score, and names the pipeline it builds on.

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?

Gives clear context: use it for a single-prompt, bounded entity comparison, and explicitly warns it is 'never a comprehensive cross-prompt visibility score.' It also discloses cost/plan requirements. It stops short of naming an alternative sibling to use when comprehensive, cross-prompt visibility is actually needed.

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