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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds transparency by explaining the internal mechanism ('Probes each entity with ai_visibility_check'), the ranking behavior, and the return contents ('ranked list with score, confidence, signal density'). This goes beyond the annotations without contradicting them.

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 main purpose, followed by mechanism + usage context, and ending with output description. Every sentence earns its place; no filler or 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?

Given the tool's moderate complexity and no output schema, the description covers purpose, process, usage scenario, and key return fields. It doesn't describe how to interpret scores or edge cases, nor does it explicitly address alternatives like 'compare_entities', but it provides sufficient context for an AI 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 description coverage is 100%, so every parameter (entities, models, _apiKey, context) has a description in the schema. The description adds minimal new parameter semantics—it reinforces that entities are 'your brand + N competitors' and mentions the output fields, but doesn't detail parameter formats or dependencies beyond what the schema already provides. Thus 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 action ('Compare AI visibility across multiple entities side-by-side'), names the resource (entities/brands), and specifies the goal (competitive AI-marketing audit). It distinguishes itself from the sibling tool 'ai_visibility_check' by explicitly mentioning it probes multiple entities and references the sub-tool, and from generic tools like 'compare_entities' by focusing on AI visibility.

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 concrete usage context: 'Useful for competitive AI-marketing audits' and 'does Claude know about us as well as our competitors?'. It implies when to use this over the single-entity 'ai_visibility_check' (when comparing multiple entities), but does not explicitly name alternatives or state exclusions, so it falls short of a 5.

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