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

Annotations already declare readOnly, idempotent, non-destructive. The description adds behavioral details beyond that: it probes with ai_visibility_check, ranks results by score, and returns a list with score, confidence, and signal density per entity. This gives useful process and output information. It does not mention rate limits or potential cost, so not a 5.

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 states the core function, the second explains mechanics and ranking, the third gives a use case and the return structure. Every sentence is valuable and there is no redundancy.

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?

For a complex tool with no output schema, the description covers the main aspects: the comparison logic, the underlying probe, the ranking, and the return format. The schema handles parameter details and annotations handle safety. This is complete enough for an agent to understand what the tool does and what to expect.

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%, and each parameter already has a clear description. The tool's description does not add anything beyond the schema about parameters, 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 clearly states the tool compares AI visibility across multiple entities side-by-side. It explicitly describes the process (probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized), distinguishing it from the single-entity sibling ai_visibility_check.

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?

Provides a concrete use case: competitive AI-marketing audits, with an illustrative question. It implies that ai_visibility_check is for single entities and this tool is for multi-entity comparison, but it does not explicitly state when not to use it or mention alternative comparison 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.