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

Annotations already indicate a safe read-only, idempotent operation. The description adds valuable context by revealing it makes multiple probes via ai_visibility_check, ranks results, and returns score/confidence/signal density. It does not contradict 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?

Three concise sentences with no redundancy: core action, how it works, and return value. Front-loaded with 'Compare AI visibility across multiple entities side-by-side.'

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 no output schema, the description states what is returned (ranked list with score, confidence, signal density). It also explains the underlying probe mechanism, making the tool's behavior understandable. The use case and entity semantics are covered.

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 covers all 4 parameters with descriptions (100% coverage). The description adds little beyond saying the first entity is the subject and others are competitors, which is already in the schema. Baseline 3 is appropriate for full schema coverage.

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 uses a specific verb ('Compare') and resource ('AI visibility across multiple entities') and clearly distinguishes from siblings like ai_visibility_check by emphasizing side-by-side, ranked comparison. It also states the output (most/least recognized) making the purpose unambiguous.

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?

It provides a clear use case ('competitive AI-marketing audits') with an example query. It implies the alternative (use ai_visibility_check for single entity) by stating it probes each entity with that tool, but doesn't explicitly name it as an alternative or state when not to use this tool.

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