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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds value by explaining the process (probing each entity, ranking, surfacing most/least recognized) and the output structure (score, confidence, signal density). No contradictions 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 (6 sentences), front-loaded with the primary action, and every sentence adds information. There is no redundancy or unnecessary detail.

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?

Despite no output schema, the description adequately explains the output format (ranked list with score, confidence, signal density) and the process. The tool is moderately complex with 4 parameters; the description covers key aspects. Missing details like max entities or error handling would push it to 5.

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 meaningful context: 'First entry treated as the subject for narrative' for entities, details on model support, and the purpose of the context parameter. This is above baseline but not exceptional.

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 compares AI visibility across multiple entities side-by-side, uses a specific verb ('compare'), and distinguishes itself from sibling tools like 'ai_visibility_check' which likely handles single entities. It also provides a concrete use case ('competitive AI-marketing audits').

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 explicitly mentions the tool is useful for competitive audits and implies it should be used when comparing multiple entities. While it does not explicitly state when not to use it, the contrast with sibling 'ai_visibility_check' provides context. A slightly higher score would require explicit exclusion cases.

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