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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context: it internally calls ai_visibility_check, ranks by score, and returns score/confidence/signal density. This goes beyond the annotations without contradicting them.

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 two sentences long, front-loaded with the core purpose, and every clause adds value. It includes an illustrative example phrasing and specifies the output components, all without unnecessary 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?

Given the tool's moderate complexity (4 params, no output schema), the description covers the main behavioral aspects: what it does, how it works internally, when to use it, and what it returns. Annotations handle safety semantics, so the description is complete for an agent to select and invoke the tool correctly.

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 baseline is 3. The description adds extra semantic value by explaining that the first entity in the 'entities' array is treated as the 'subject' for narrative, while the rest are competitors. This meaningfully augments the schema's simple array description.

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 a specific verb ('Compare') and resource ('AI visibility across multiple entities side-by-side'), and distinguishes itself from sibling tools by mentioning it probes with ai_visibility_check and ranks results. It also provides a concrete example use case, making the purpose unambiguous.

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 context for use ('competitive AI-marketing audits') and implies when to use it over ai_visibility_check (for multiple entities). It does not explicitly state when not to use it or name alternative tools, but the use case is well-defined enough.

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