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

The description discloses that it internally probes each entity with 'ai_visibility_check', ranks by score, and returns a list with score, confidence, and signal density. Annotations already indicate read-only, idempotent, and non-destructive behavior, and the description adds valuable behavioral context without contradiction.

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 four sentences, efficiently covering purpose, usage, internal mechanism, and return format without unnecessary repetition 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?

With no output schema, the description adequately describes the return format (ranked list with score, confidence, signal density). All 4 parameters are explained in context, use case is clear, and the tool's behavior is fully specified.

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 coverage is 100% but the description adds meaning: it explains 'entities' array where first entry is the subject, mentions the internal call to ai_visibility_check, and clarifies the 'context' parameter for disambiguation. This enriches the parameter understanding beyond schema 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 'Compare AI visibility across multiple entities side-by-side', using a specific verb and resource. It distinguishes itself from sibling tool 'ai_visibility_check' (single entity) and from generic 'compare_entities' by focusing on AI visibility and ranking.

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 clear use case ('competitive AI-marketing audits') and example question. It implies when to use this tool (comparing multiple entities) but does not explicitly state when not to use it or name alternative tools for other scenarios.

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