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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds behavioral context: it probes each entity, ranks by score, and returns a ranked list with specific output fields (score, confidence, signal density). This goes beyond the raw annotation hints, but could mention rate limits or error 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?

Three sentences deliver the core purpose, mechanism, use case, and return format without unnecessary words. Every clause earns its place, and the key verb and resource appear in the first clause, making it front-loaded and scannable.

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?

With no output schema, the description correctly takes on the burden of explaining the return value ('ranked list with score, confidence, signal density per entity'). It also covers the purpose, underlying mechanism, and a concrete example use case. It doesn't discuss edge cases like invalid entity counts or API key errors, but these are minor for a read-only comparison tool.

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 each parameter, including the special 'first entry treated as subject' rule. The description reinforces 'your brand + N competitors' but doesn't add new semantic details beyond what the schema already provides, so 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 opens with a specific verb and resource: 'Compare AI visibility across multiple entities side-by-side.' It clearly distinguishes from siblings like ai_visibility_check (single-entity probe) and compare_entities by detailing the ranking and most/least recognized output, so an agent knows exactly what this tool produces.

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 ('competitive AI-marketing audits') and quotes an example question. It also mentions that it probes with ai_visibility_check, implicitly differentiating from that single-entity alternative. However, it doesn't explicitly state when NOT to use it or compare to other sibling tools like compare_entities.

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