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

No annotations are provided, so the description carries the full burden. It discloses that multiple AI engines are used (one web-grounded), the output is a JSON report, and there is a per-call cost of $0.25 USDC via x402. However, it does not cover potential errors, timeouts, rate limits, or what happens if the brand is not mentioned.

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 two sentences that are front-loaded with the core action ('See what AI models say about a brand right now') and then list capabilities concisely. It is informative without being verbose.

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?

There is no output schema, so the description compensates by listing the report contents (mention, position, sentiment, descriptors, gaps, recommendations). Combined with the three described parameters and cost disclosure, it provides a fairly complete picture, though exact JSON structure is omitted.

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%, so the schema already describes parameters. The description adds value by explaining that 'domain' improves accuracy and 'category' is used for best-of prompts, providing context beyond the schema.

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 it runs category and reputation prompts across multiple AI engines to assess brand visibility, including mention detection, competitor position, sentiment, descriptors, gaps, and recommendations. It specifies a concrete verb and resource, distinguishing it from sibling tools like web-search or news-search.

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 usage for brand analysis via AI model outputs but provides no explicit guidance on when to use this tool versus alternatives (e.g., web-search or news-search). No exclusions or when-not-to-use scenarios are mentioned.

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