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

Annotations already declare readOnly, idempotent, non-destructive. Description adds that it internally calls ai_visibility_check for each entity, ranks by score, and returns 'score, confidence, signal density'. 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?

Three sentences, front-loaded with action and purpose, no redundant words. Every sentence adds value: comparison, method, use case, return format.

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?

No output schema, but description clearly states returns 'ranked list with score, confidence, signal density per entity'. Covers optional parameters and use case. Lacks details on error handling or pagination, but acceptable for this complexity.

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). Description adds meaning by explaining how parameters are used together: 'probes each entity with ai_visibility_check', context is shared across probes, models optional. This enriches understanding beyond the schema.

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 explicitly states 'compare AI visibility across multiple entities side-by-side', uses specific verb 'probes' with 'ai_visibility_check', and clearly distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison).

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?

Provides clear context: 'useful for competitive AI-marketing audits' with an example question. Implicitly differentiates from ai_visibility_check by stating it probes multiple entities. However, lacks explicit when-not-to-use or alternative tool references beyond the sibling list.

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