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

Annotations declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint as false. The description adds process details (probes each entity, ranks, returns scores) without contradicting annotations. It could mention auth requirements more explicitly but the _apiKey param covers it.

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 two sentences long, front-loaded with the main action, includes a concrete example, and does not contain any redundant or superfluous information.

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?

Despite lacking an output schema, the description explains the return value as a 'ranked list with score, confidence, signal density per entity.' It also mentions constraints (2-8 entities) and the tool's dependency on ai_visibility_check, providing a complete mental model for the agent.

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 has 100% description coverage for all 4 parameters. The description adds meaning beyond schema by explaining that the first entity is treated as the 'subject' for narrative, clarifying optional model and _apiKey usage, and providing context for the entities parameter.

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 tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides an example use case and distinguishes from the sibling ai_visibility_check by being a composite tool for multiple 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 says 'Useful for competitive AI-marketing audits' and gives an example question. It implies the tool is for multi-entity comparison, but does not explicitly state when not to use it or mention alternatives like using ai_visibility_check for a single entity.

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