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

Annotations already declare safe read-only/idempotent behavior. The description adds valuable operational detail: it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. This goes beyond the annotations and fully discloses 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?

Three sentences, front-loaded with the core comparison purpose, then adding mechanistic detail and a concrete use case. No wasted words; every sentence contributes unique information.

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?

Despite having no output schema, the description explicitly states the return structure (ranked list with score, confidence, signal density) and explains the internal probing/ranking behavior. Combined with full schema coverage and annotations, the description fully equips an agent to understand the tool's inputs, outputs, and mechanics.

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% and the schema already documents each parameter, including that the first entity is the 'subject'. The description adds only marginal color (e.g., 'your brand + N competitors') but does not provide additional semantic depth beyond what the schema already states, so the 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 states a specific verb+resource+scope: 'Compare AI visibility across multiple entities side-by-side' and further distinguishes itself from the sibling ai_visibility_check by explicitly mentioning it as the underlying probe and adding ranking/comparison behavior. This makes the tool's purpose unambiguous.

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 clear use case: 'competitive AI-marketing audits' with an illustrative question, and implies multi-entity scenarios. However, it does not explicitly state when to use an alternative (e.g., ai_visibility_check for a single entity), leaving a minor gap.

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