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

With no annotations, the description carries the full burden. It clearly discloses the behavior: it queries multiple AI models for recommendations and outputs a score, mention rate, and alternative names. It also transparently notes the cost ($0.09 via x402). It lacks details on data freshness, methodology, or prerequisites, but the core behavioral traits are well covered.

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 three sentences, front-loaded with price and the core question, then outputs, then use case and uniqueness. Every sentence adds value, with no repetition or fluff. It is efficiently structured and easy to scan.

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?

Since there is no output schema, the description compensates by explaining the return values: AI-visibility score 0-100, mention rate, and alternative projects. It also covers the input context, use case, and cost. Missing details like x_payment behavior or request flow are minor given the tool's simplicity.

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 67% (project and category have descriptions, x_payment does not). The description adds context to the category parameter by framing it as 'the best in its category' and clarifying that project refers to token/protocol/chain. However, it does not explain the x_payment parameter, and the added semantics are modest 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 explicitly states what the tool does: it checks whether ChatGPT/Perplexity/Google AI recommend a given token, protocol, or chain for a category, and returns a visibility score, mention rate, and alternative projects. This is a specific verb+resource+output and differentiates from sibling tools by claiming to be 'The only AI-recommendation data for crypto projects.'

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 tool is a 'narrative/attention signal for crypto trading and research agents,' suggesting when it should be used. However, it does not explicitly state when to use this tool versus sibling alternatives like ai_visibility_index or ai_visibility_signal, nor does it mention exclusions or preferred scenarios beyond that use case.

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