APIbase

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

With no annotations provided, the description carries the full burden. It discloses the return behavior ('returns per-model visibility scores') and implies a read-only operation via 'Test', but lacks operational details such as rate limits, caching behavior, error cases for invalid domains, or response format specifics.

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?

Single sentence efficiently packs the action, specific AI models tested, the concept being measured, and the return value structure. Zero redundant words; 'AIPush' suffix appropriately brands the return context without unnecessary verbosity.

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 single-parameter analysis tool with no output schema, the description adequately covers the core functionality and conceptual return value. It could be improved by mentioning error handling or score format (numeric ranges), but provides sufficient context for an agent to select and invoke the tool 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%, with the 'website_domain' parameter fully documented in the schema including an example. The description references the concept ('a brand') but does not add parameter-specific semantics beyond the schema, warranting the baseline score for high-coverage schemas.

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 uses specific verb 'Test' with clear resource scope ('whether AI assistants... know about and recommend a brand') and explicitly names the target models (ChatGPT, Perplexity, Gemini). It distinguishes from sibling 'aipush.analysis.profile' by specifying 'visibility scores' as the output focus.

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 through specificity—use this to check brand visibility across AI models—but provides no explicit when-to-use guidance or comparison to alternatives like 'aipush.analysis.profile'. The agent must infer the distinction based on the 'visibility' vs 'profile' naming.

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