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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, and the description adds meaningful behavioral context: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also discloses the return format (ranked list with score, confidence, signal density), providing value beyond 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?

Three sentences: the first declares the core function, the second details the workflow and ranking, the third explains the use case and output. Every sentence earns its place with no redundancy or filler.

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 annotations covering the safety profile and schema covering all parameters, the description fills the remaining gaps: it explains the internal probing mechanism and specifies the return structure (score, confidence, signal density), which is essential given there is no output schema. Complete for a 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% — all four parameters are fully described in the schema, including the nuance that the first entity is treated as the subject. The description adds no additional parameter-level meaning beyond what the schema already provides, so the baseline 3 applies.

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 a specific verb ('Compare'), resource ('AI visibility'), and scope ('across multiple entities side-by-side'). It distinguishes from the sibling ai_visibility_check by explicitly noting it probes each entity and ranks results, making it obvious this is the multi-entity comparison variant.

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?

Explicitly mentions a use case ('competitive AI-marketing audits') and gives a concrete example query. It doesn't explicitly name alternative tools or state when not to use it, but the phrase 'across multiple entities' and the workflow of comparing against competitors clearly separates it from single-entity alternatives.

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