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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's burden is lower. The description adds value by revealing that it internally probes each entity with 'ai_visibility_check' and returns a ranked list with specific fields (score, confidence, signal density). This goes beyond annotations to explain the tool's internal behavior and output structure.

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 (4 sentences) and front-loaded with the main purpose. Every sentence adds value: purpose, behavior, use case, and output format. There is no redundancy or fluff. The structure is well-organized and easy to scan.

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, internal sub-tool call, no output schema), the description is complete. It explains the return format (ranked list with score, confidence, signal density), the role of parameters, and the use case. Annotations provide safety context, and the description fills the gaps left by the lack of an output schema.

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%, but the description adds meaning beyond parameter descriptions. It notes that the first entity in the array is treated as the 'subject' for narrative purposes, and explains the 'context' parameter as disambiguating common names. This provides additional semantic context that helps an agent use the parameters correctly.

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 purpose: 'Compare AI visibility across multiple entities side-by-side'. It specifies the verb (compare/probe), the resource (AI visibility), and the outcome (ranked list). It distinguishes from sibling tools by explicitly mentioning that it calls 'ai_visibility_check' and frames it for competitive audits, differentiating it from single-entity checks or generic comparison tools.

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 provides a clear use case: 'Useful for competitive AI-marketing audits' and gives an example question. It implies when to use (multi-entity comparison) and suggests the first entity as subject. However, it does not explicitly state when not to use or directly contrast with sibling tools like 'compare_entities' or 'ai_visibility_check', leaving some implicit guidance.

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