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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds that it performs a multi-probe comparison via ai_visibility_check and returns a ranked summary, which goes beyond the annotations and clarifies the internal process.

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?

Four concise sentences: purpose, mechanics, use case, and output format. Every sentence earns its place, with no repetition or filler.

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 with only 4 params and no output schema, the description covers the behavior, return format (ranked list with score/confidence/signal density), and use case. Combined with rich annotations and schema, it leaves no critical gaps.

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 description coverage is 100% with detailed parameter docs, including supported models, optional API key, shared context, and entity ordering semantics. The description reinforces the 'brand + competitors' idea but adds no extra parameter meaning 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?

Clearly states the tool compares AI visibility across entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. This specific verb+resource+outcome distinguishes it from sibling tools like ai_visibility_check (single probe) 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?

Explicitly frames usage for competitive AI-marketing audits and provides a concrete example question. While it doesn't name alternative tools or exclusions, the context is specific and actionable, making it easy to decide when to use.

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