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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds context that the tool internally calls ai_visibility_check for each entity, ranks by score, and returns confidence/signal density—useful behavioral insight beyond the annotations. No contradictions found.

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, front-loaded with the core action, and every sentence earns its place. It avoids redundancy with the schema and even includes a concrete example without becoming verbose.

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?

Even without an output schema, the description explicitly states the return value (ranked list with score, confidence, signal density per entity), and covers the operational flow (probing, ranking, surfacing). It provides enough context for an agent to understand what to expect and when to invoke it, especially given the rich schema and annotations.

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?

The input schema already provides 100% parameter coverage, so the baseline is 3. The description adds valuable semantic detail by stating the first entity in 'entities' is treated as the subject for narrative, which is not in the schema. It also clarifies that 'models' can be omitted for the default worker-ai, aligning with the schema's optional description.

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, with a specific verb ('Compare') and resource ('AI visibility'). It also distinguishes itself from the sibling ai_visibility_check by emphasizing multi-entity comparison and ranking, and mentions the underlying probe mechanism.

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 concrete use case ('competitive AI-marketing audits') with an example query, and implies it is the multi-entity counterpart to ai_visibility_check. However, it does not explicitly state when not to use the tool or name alternative tools for single-entity checks, leaving some room for interpretation.

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