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

The description adds behavioral details beyond annotations: it calls ai_visibility_check internally, returns a ranked list with score, confidence, and signal density. Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. No contradictions. Could mention potential rate limits or error behavior, but current details are useful.

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 plus an example line, front-loaded with the main purpose. Every sentence adds value: purpose, mechanism, use case. No wasted words.

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 no output schema, the description adequately describes the return format (ranked list with score, confidence, signal density per entity). It covers all essential aspects: purpose, usage, parameters, and output. For a 4-parameter tool with 100% schema coverage, this is complete.

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% (all parameters described). The description adds semantic value: the first entity is treated as the 'subject' for narrative, and 'Omit for just workers-ai' clarifies models usage. This extra context improves parameter understanding beyond the schema alone.

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 the tool compares AI visibility across multiple entities side-by-side, specifying the verb 'Compare', the resource 'AI visibility', and how it ranks and surfaces most/least recognized entities. It differentiates from siblings like ai_visibility_check (which likely handles single entities) and compare_entities (which might be generic).

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 provides a clear use case: 'competitive AI-marketing audits' with an example question. It implies when to use (comparing multiple entities) and mentions the internal reliance on ai_visibility_check, linking to a sibling. It lacks an explicit when-not clause but still gives strong guidance.

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