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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds value beyond that by disclosing that it probes with ai_visibility_check, ranks by score, and returns a structured result with score, confidence, and signal density. This gives operational context without contradicting the 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 three sentences: first states the core function, second explains the mechanism, third provides a use case and output summary. No wasted words; every sentence adds distinct value and the main purpose is front-loaded.

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?

The tool is a composite read-only operation with no output schema, so the description carries the burden of explaining what it returns, which it does (ranked list, score, confidence, signal density). The schema covers all parameters and constraints, and the annotations cover safety. The description is sufficient for an agent to understand when and how to invoke it.

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 each parameter described in detail, so the baseline is 3. The description adds minimal semantic value beyond the schema—it mentions the first entity as subject and calls ai_visibility_check, but these are already in the schema. No meaningful additional parameter explanation is needed.

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 uses a specific verb and resource: 'Compare AI visibility across multiple entities side-by-side.' It clearly distinguishes itself from the single-entity sibling ai_visibility_check by emphasizing the multi-entity comparison and ranking behavior, and it ties to a concrete use case with an example.

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?

Provides clear context: 'Useful for competitive AI-marketing audits' and an example. However, it does not explicitly name alternatives or state when not to use it (e.g., for a single entity). The sibling list and description imply multi-entity use, but the guidance is not fully explicit about exclusions.

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