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

The description adds significant behavioral details beyond the annotations: it explains the process (probing each entity with ai_visibility_check, ranking), and specifically that the first entity is treated as the 'subject' for narrative. It also reveals output fields (score, confidence, signal density). The annotations (readOnlyHint, idempotentHint) are consistent, and the description enriches understanding of the tool's behavior.

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?

At four sentences, the description is concise and well-structured. The first sentence states the primary purpose, the second details the process, the third provides a practical use case, and the fourth describes the output. No sentences are wasted, and key information is front-loaded.

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 (4 parameters, comparison logic) and the absence of an output schema, the description is complete. It explains the input semantics, the processing logic, and the output structure (ranked list with score, confidence, signal density). The comprehensive annotations further cover safety and idempotency, leaving no major gaps.

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 has full description coverage (100%) for all 4 parameters. The description adds value by explaining that entities should be 'your brand + N competitors' and that the first entity is treated as the subject. It also contextualizes the purpose of models and context, going beyond the schema's literal 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's core function: 'Compare AI visibility across multiple entities side-by-side.' It specifies that it probes each entity with ai_visibility_check, ranks by score, and surfaces the most/least recognized. This distinguishes it from the sibling tool ai_visibility_check (single entity check) and other compare tools by focusing 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?

The description provides a concrete use case: 'useful for competitive AI-marketing audits' with an example question. It implies this tool is for comparing multiple entities, implicitly differentiating from single-entity checks. However, it does not explicitly state when not to use it or list alternatives, missing the highest standard.

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