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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. Description adds value by explaining the tool probes each entity, ranks by score, and surfaces most/least recognized. It also describes the output (ranked list with score, confidence, signal density). No contradictions 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?

Description is concise (three sentences) with no wasted words. Front-loaded with purpose and action. Each sentence adds value: purpose, mechanism, use case, output.

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 rich annotations and full schema coverage, description is complete. It explains return values (ranked list) despite no output schema. Covers all necessary aspects for the agent to use 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 baseline is 3. Description does not add significant meaning beyond schema; it rephrases parameter descriptions (e.g., 'entities' as brand names) and provides a usage example, but does not add new constraints or format details.

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 clearly states the verb 'compare' and resource 'AI visibility' across multiple entities. It explains the process of probing each entity with ai_visibility_check and ranking results. The purpose is well-distinguished from sibling tool ai_visibility_check, which is for single entities.

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?

Description explicitly states 'Useful for competitive AI-marketing audits' and provides a concrete example. It implies when to use (comparing multiple entities) but does not explicitly mention when not to use or alternatives. The context is clear enough for selection.

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