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

Annotations already mark the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds significant behavioral details: it probes each entity via ai_visibility_check, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, and signal density per entity. It also clarifies that the first entity is treated as the 'subject' for narrative purposes. 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?

The description is a single paragraph of three sentences. The first sentence states the main action, the second describes the process, and the third gives a use case example. It is front-loaded, efficient, and contains no filler. Every sentence adds value.

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 (4 params, no output schema), the description covers all essential aspects: it explains the internal call to ai_visibility_check, the ranking process, the output format (ranked list with score/confidence/signal density), and the special treatment of the first entity. The annotations handle safety/readOnly concerns. No gaps remain.

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?

The input schema has 100% description coverage, so the baseline is 3. The description does not add new parameter-specific information beyond what is in the schema; it repeats the role of entities and the optional models/apiKey/context. While it contextualizes the workflow, it does not deepen parameter understanding beyond the schema's own 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's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies the verb (compare, probe, rank), the resource (AI visibility of entities), and distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison) by detailing the internal use of ai_visibility_check and ranking by score.

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 provides a concrete use case: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"'. It gives context for when to use the tool but does not explicitly state when not to use it or list alternative tools. However, the sibling tool list allows agents to infer alternatives, and the description implies single-entity checks should use ai_visibility_check.

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