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

The description goes well beyond the annotations by explaining the internal process: it calls ai_visibility_check per entity, ranks by score, and surfaces most/least recognized. It also discloses the output structure (ranked list with score, confidence, signal density) and that context is shared across probes. No contradictions with annotations.

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, each earning its place: purpose, method/usecase, and return value. It is front-loaded and wastes no words, making it easy for an agent to parse quickly.

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 the tool's moderate complexity and absence of an output schema, the description is complete: it explains what it does, how it works (via ai_visibility_check), why to use it, and what it returns (ranked list with metrics). This provides enough context for an agent to select and invoke it 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 coverage is 100%, so baseline is 3. The description adds minimal parameter meaning beyond the schema: it reiterates that context is shared and notes first entity is the 'subject', but these are already in the schema. The description's mention of probing mechanics doesn't enhance parameter understanding.

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 purpose with specific verbs ('Compare', 'Probes', 'ranks') and a well-defined resource: AI visibility across multiple entities. It distinguishes itself from sibling tool ai_visibility_check by focusing on side-by-side multi-entity comparison and ranking.

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 clear usage context: competitive AI-marketing audits with an example query. It implies it is the multi-entity alternative to ai_visibility_check by mentioning it probes each entity with that tool. However, it does not explicitly state when not to use this tool or name exclusion criteria, keeping it just short of a 5.

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