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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context beyond those: it probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, and signal density. This adds transparency regarding internal mechanism and output, without contradicting the 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 concise and front-loaded: the first sentence states the core action, the second explains the mechanism, the third gives a use case, and the fourth lists return fields. Every sentence earns its place with no redundancy or fluff.

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 there is no output schema, the description appropriately describes the return value ('ranked list with score, confidence, signal density per entity'). It also explains the tool's relationship to sibling ai_visibility_check and covers the competitive audit use case. This is complete for a tool with 4 parameters and a single required field.

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%, with each parameter (models, _apiKey, context, entities) already described in the input schema. The description does not add new parameter semantics beyond what the schema states (e.g., first entity as subject, rest as competitors). Since schema covers all params, baseline of 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 tool's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies a concrete verb ('Compare'), a resource ('AI visibility'), and the scope ('multiple entities'). It also distinguishes from sibling tool ai_visibility_check by emphasizing multi-entity comparison, ranking, and output specifics.

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 clear context for use, stating it is 'Useful for competitive AI-marketing audits' and gives an example with a concrete query. It implicitly differentiates from single-entity sibling ai_visibility_check by focusing on multiple entities, but it does not explicitly state when not to use this tool or name alternatives as exclusions.

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