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

Annotations already declare read-only, open-world, idempotent, and non-destructive. The description adds value by revealing that it internally calls ai_visibility_check for each entity and returns a ranked list with score, confidence, signal density. No contradictions.

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, front-loaded with the main action, and every sentence adds essential information without redundancy. Highly efficient.

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 good annotations and 100% schema coverage, the description still provides needed behavioral context (internal call to ai_visibility_check, ranking, return fields). No gaps are apparent.

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% with descriptions for all 4 parameters. The description adds context beyond schema by noting that the first entity in the array is treated as the 'subject' for narrative and rest as competitors, which aids correct usage.

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 it compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic).

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 mentions usefulness for competitive AI-marketing audits and provides an example query. It implies when to use (multi-entity comparison) but does not explicitly state when not to use or name alternatives, though sibling ai_visibility_check is obvious.

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