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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations by explaining the internal mechanism (probes each entity with ai_visibility_check), ranking logic, and output structure (score, confidence, signal density). This exceeds the baseline given the strong annotation coverage.

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, front-loaded with the core purpose, and each sentence earns its place: purpose, usage context with example, and return values. No wasted words.

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, 1 required, no output schema), the description fully covers purpose, usage, parameter semantics, and expected return format. It is complete for an AI agent to select and invoke correctly without additional documentation.

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% with descriptive parameter docs. The description adds extra semantic value by explaining that the first entity is treated as the subject for the narrative and the rest as competitors, and by noting the shared context use case. This goes beyond the schema, so a 4 is appropriate.

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 AI visibility') and resource ('multiple entities side-by-side'), and explicitly distinguishes itself from related tools like ai_visibility_check by framing it as a comparative audit across brands/competitors. The example query further clarifies the purpose.

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 clear context for when to use the tool ('Useful for competitive AI-marketing audits') and implies it is an alternative to running ai_visibility_check individually for each entity. It does not explicitly state exclusions or when not to use it, but the example and mention of ai_visibility_check give sufficient guidance.

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