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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context: it probes each entity via ai_visibility_check, ranks by score, and returns per-entity score, confidence, and 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?

The description is concise and front-loaded: it states the primary action first, then explains the method, use case, and return format. Every sentence adds value with no redundant or vague phrasing.

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 no output schema, the description appropriately explains the return value (ranked list with score, confidence, signal density). It also references the internal mechanism and use case. Lacks details on edge cases or error conditions, but these are not critical for basic understanding.

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%, so the description doesn't need to explain parameter syntax. It does add the nuance that the first entity is treated as the 'subject' for narrative, but this is already present in the schema's entities description. Minimal added value 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?

The description clearly states the tool's function: comparing AI visibility across multiple entities side-by-side. It distinguishes itself from the sibling tool ai_visibility_check by emphasizing multi-entity comparison and ranking, and mentions its output of a ranked list.

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 explicit use case for competitive AI-marketing audits with an example scenario. It implies that ai_visibility_check is the single-entity alternative but does not explicitly list exclusions or when not to use.

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