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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint: false. Description adds meaningful behavioral context: it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. No contradiction with 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, front-loaded with purpose, includes an example and return format. 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?

With 4 params and no output schema, the description compensates by stating the return structure (ranked list with score, confidence, signal density) and providing a concrete use case. Missing edge cases or rate limits, but adequate for moderate complexity.

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 baseline is 3. Description reinforces the entities parameter ('your brand + N competitors') and the 'first entry treated as subject' rule, but adds no new parameter information beyond the schema.

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?

Description uses a specific verb and resource: 'Compare AI visibility across multiple entities side-by-side.' It distinguishes itself from sibling ai_visibility_check by focusing on multi-entity comparison and from compare_entities by targeting AI visibility specifically.

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 clear use case: 'Useful for competitive AI-marketing audits' with an example query. It implies single-entity checks would use ai_visibility_check, but does not explicitly state when not to use this tool or name alternatives.

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