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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description goes beyond these by disclosing the probing process (calls ai_visibility_check per entity), the ranking logic, and the return structure (ranked list with score, confidence, signal density). This adds behavioral detail not encoded in the annotations, though it does not cover potential call limits or external dependencies.

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 three sentences long, front-loaded with the core purpose, and every sentence adds value: function, process, use case, and output. There is no redundancy or filler, making it highly concise and well-structured.

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 no output schema, the description adequately explains the return values (ranked list with score, confidence, signal density). It also covers the use case and the probing workflow. It does not mention the optional models or _apiKey parameters, but those are fully documented in the schema, and the description covers the essential behavior and outputs.

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%, so the schema already documents all parameter meanings. The description adds minimal extra semantics: it clarifies that entities include 'your brand + N competitors' and mentions 'probes each entity', but these largely echo the schema's 'First entry treated as the subject' and the parameter descriptions. No substantial 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 specifies the action ('Compare'), the resource ('AI visibility across multiple entities'), and distinguishes it from the sibling tool ai_visibility_check by emphasizing the multi-entity comparative nature ('your brand + N competitors', 'ranks by score').

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 includes a concrete use case ('competitive AI-marketing audits') and explains the workflow ('Probes each entity with ai_visibility_check'). It does not explicitly name alternatives, but the phrase 'side-by-side' and 'multiple entities' implies when this tool is appropriate versus the single-entity sibling, providing clear context without formal exclusions.

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