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

The description discloses that it probes each entity with ai_visibility_check, a read-only operation consistent with annotations (readOnlyHint, idempotentHint). It adds behavioral context about returning a ranked list with score, confidence, and signal density, beyond what annotations provide.

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 concise paragraph of four sentences, front-loaded with the core purpose and efficiently packed with all necessary information without any fluff.

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?

Despite no output schema, the description fully explains the return value (ranked list with score, confidence, signal density per entity). All input parameters are covered in the schema, and the description adds necessary context for usage. The tool's complexity is well-addressed.

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%, and the description adds critical semantic information: the first entity is treated as the 'subject' for narrative and rest as competitors. It also provides context for models and _apiKey parameters, enhancing understanding beyond schema descriptions.

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, using verbs like 'Compare', 'Probes', 'ranks', and 'surfaces'. It distinguishes from sibling tools by explaining it is a batch version of ai_visibility_check, and provides a concrete example.

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 specifies it is useful for competitive AI-marketing audits and gives an example question. It implies when to use, but does not explicitly state when not to use or name alternatives, though the presence of ai_visibility_check as a sibling provides implicit guidance.

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