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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context: it describes the process (probes each entity with ai_visibility_check, ranks, surfaces results) and hints at dependencies (requires _apiKey for anthropic model). It does not contradict annotations and provides useful process details beyond structured data.

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?

The description is three sentences with no wasted words. The first sentence states the core action, the second details the process, and the third provides a use case and return format. It is front-loaded and efficiently covers all necessary 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?

Given there is no output schema, the description explains the return format: 'ranked list with score, confidence, signal density per entity.' It also covers all inputs, including the role of the first entity and the optional context parameter. The description is complete for a tool with 4 parameters and no output schema.

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%, so the description does not need to compensate for missing schema details. However, the description adds extra semantics: it explains that the first entity in 'entities' is treated as the 'subject' for narrative, and that 'context' disambiguates common names. This adds value beyond the schema descriptions.

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, probes each with ai_visibility_check, ranks by score, and surfaces which is most/least recognized. It differentiates itself from sibling tools like ai_visibility_check and compare_entities by being specifically for multi-entity competitive AI presence audits.

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 explicitly states it is useful for competitive AI-marketing audits and provides an example question. It implies when to use (comparing multiple entities' AI visibility) and implicitly differentiates from single-entity checks. However, it does not explicitly state when not to use or list alternative tools.

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