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

Annotations already declare read-only and idempotent behavior, allowing the description to focus on added context. It discloses that the tool probes each entity with ai_visibility_check and returns a ranked list with score, confidence, and signal density, which goes beyond annotations to explain internal behavior and output shape.

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 (three sentences) and front-loaded with the core purpose. Each sentence adds value: the function, the mechanism, and a usage example. No wasted words.

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?

For a tool that aggregates multiple probes, the description covers the essential input semantics (entities, models, context) and output structure (ranked list with score, confidence, signal density). With no output schema, this suffices for an agent to correctly invoke the tool and interpret results.

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 parameters are fully documented. The description adds meaningful nuance beyond the schema, such as the first entity being treated as the 'subject' for narrative and the free default for models. This clarifies intended usage that the schema's descriptions do not fully convey.

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 entities side-by-side, probing with ai_visibility_check, and ranking results. It distinguishes itself from the singular ai_visibility_check and generic compare_entities by focusing on competitive AI audits, with a concrete example ('does Claude know about us as well as our competitors?').

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 gives a clear use case (competitive AI-marketing audits) and implies the alternative of using ai_visibility_check for single entities. However, it does not explicitly state when not to use this tool or explicitly compare with sibling tools like compare_entities, so it lacks explicit exclusions.

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