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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context: it calls ai_visibility_check per entity, ranks results, and returns a structured list with score, confidence, and signal density. It does not disclose potential API costs or rate limits for the optional Anthropic model, but this is a minor gap given the 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, front-loaded with the primary purpose, then explains the mechanism and output. Every sentence earns its place with no fluff or redundancy. It is concise yet information-dense.

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 (multi-entity probing, optional models, context disambiguation), the description covers the main purpose, behavior, and output format. It lacks an explicit output schema but compensates by describing the returned data ('ranked list with score, confidence, signal density per entity'). It could mention the optional Anthropic API key constraints, but the schema covers that, so the description is sufficiently 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 description coverage is 100%, so the schema already fully documents all parameters. The description itself does not add meaningful parameter semantics beyond what the schema provides (e.g., it does not mention that the first entity is treated as the subject, though the schema says this). It mentions 'entities' implicitly but adds no extra detail. Therefore, a baseline score of 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 purpose: 'Compare AI visibility across multiple entities side-by-side.' It then specifies the mechanism ('Probes each entity... with ai_visibility_check, ranks by score') and outputs ('Returns ranked list with score, confidence, signal density per entity'). This distinguishes it from the sibling tool ai_visibility_check, which presumably checks a single entity.

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 gives a specific use case ('competitive AI-marketing audits') and an example question, making it clear when to use this tool. It implies that this tool is for multi-entity comparison, while ai_visibility_check is for a single entity, but it does not explicitly state 'when not to use' or name alternative tools for single-entity checks. Thus it provides strong guidance but stops short of explicit exclusions.

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