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

Discloses that the tool internally probes each entity using ai_visibility_check and returns a ranked list with score, confidence, and signal density. Aligns with the readOnly/idempotent annotations and adds context about composite behavior and output structure, though it doesn't discuss potential cost or latency.

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 concise and front-loaded, starting with the core action, then detailing the process, a use case, and the return value. Each sentence contributes information without redundancy.

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?

The description explains the tool's composite behavior, its use case, and its return format, which compensates for the lack of an output schema. It provides sufficient context for an agent to decide when and how to invoke the tool, making it 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% with detailed descriptions for all parameters, including the entities semantics. The description adds no new parameter-specific information beyond the schema, so per the guideline, baseline 3 is appropriate.

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's function: comparing AI visibility across multiple entities, using ai_visibility_check, ranking by score, and identifying most/least recognized. It distinguishes itself from single-entity tools like ai_visibility_check by emphasizing multi-entity comparison, and from generic compare_entities by focusing specifically on AI presence.

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?

Provides a clear use case (competitive AI-marketing audits) with an illustrative query, implying when this tool is appropriate. However, it does not explicitly exclude alternatives or mention when to use a single-entity tool instead, earning a 4 rather than a 5.

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