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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds useful behavioral detail beyond annotations: it probes each entity with ai_visibility_check, ranks results, and returns score, confidence, and signal density per entity. It does not mention cost/rate-limit implications of multi-probe execution, but the core behavior is transparent.

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 with no filler. The first sentence states the core action, the second explains the mechanism, and the third provides a concrete use case plus output shape. Every sentence earns its place and the key differentiator is front-loaded.

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 appropriately explains the return value: a ranked list with score, confidence, and signal density per entity. It covers the main intended scenario and behavior. Minor gaps remain around error cases, cost/rate-limit expectations for multi-probe execution, and explicit alternative guidance, but the description is largely complete for an agent to select and invoke the tool correctly.

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%, so the schema already documents all four parameters and their semantics. The description adds no new parameter-level detail beyond what the schema provides, such as the role of the first entity or the optional Anthropic key. Baseline 3 is appropriate since the schema carries the parameter burden.

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 uses a specific verb and resource: 'Compare AI visibility across multiple entities side-by-side.' It further distinguishes this tool from the single-entity ai_visibility_check by stating that it probes each entity, ranks by score, and surfaces which entity is most/least recognized. The purpose is unambiguous and clearly separated from sibling tools.

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 concrete use case: competitive AI-marketing audits, with the example 'does Claude know about us as well as our competitors?'. It also names ai_visibility_check as the underlying probe mechanism, implying when this tool is the multi-entity counterpart. It does not explicitly state when not to use it versus compare_entities or other siblings, but the context is clear enough for an agent to route appropriately.

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