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

Annotations already include readOnlyHint=true, and the description adds functional context by specifying the covered AI models (ChatGPT/Perplexity/Gemini/Claude) and the 'where it's invisible' aspect. However, it does not disclose return format, number of prompts generated, or any usage caveats. The added context is useful but not rich enough to exceed the baseline.

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 a single, dense sentence that is front-loaded with the core function ('Generate prompts') and immediately specifies the purpose. There is no redundant wording or repetition of the title/schema information, making it an appropriately sized and well-structured description.

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?

Given the tool's simplicity (4 params, no output schema), the description covers the primary purpose and scope. However, it does not clarify the output format (e.g., a list of prompts vs. a report with visibility gaps), which is a notable omission for an agent that cannot infer from an output schema. Yet the description is close to fully contextual for a prompt-generator tool.

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 50%, with category and competitors having descriptions, while brand and audience lack them. The tool description indirectly clarifies 'brand' as the subject of visibility testing, but does not explain 'audience' or add detail beyond the schema. Since coverage is exactly at 50% and the description minimally compensates, a mid-range score 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?

The description clearly specifies a unique action: generating prompts to test brand recommendation across four named AI models and to identify where a brand is invisible. This verb+resource formulation distinguishes it from sibling generators (e.g., ad-copy-generator, blog-outline-generator) which focus on content creation rather than brand visibility testing.

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 implies the use case—checking brand visibility in AI responses—which provides clear context for when to choose this tool. However, it does not explicitly mention exclusions or alternatives, such as comparing with aeo-checker for broader answer-engine optimization. The context is clear enough for selection but lacks formal alternative guidance.

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