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

Annotations already declare readOnly/openWorld/idempotent and non-destructive, so the description adds valuable behavioral context: it probes each entity via ai_visibility_check, ranks by score, and returns a ranked list with specific fields. It does not contradict annotations and goes beyond them by describing the internal process 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 three sentences long, front-loaded with the core action, then explains the mechanism, provides a use case, and states the output. Every sentence serves a distinct purpose without redundancy, making it highly efficient and scannable.

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, no output schema), the description provides a complete picture: what it does, how it works (probing with ai_visibility_check), why to use it (competitive audits), and what it returns (ranked list with score, confidence, signal density). No critical operational aspect is left unexplained.

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% per context, with each parameter (models, _apiKey, context, entities) having a description. The tool description adds limited parameter-level meaning beyond what the schema already provides, only reinforcing the 'first entry treated as subject' behavior. It meets the baseline but does not substantially elevate parameter understanding.

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') and resource ('AI visibility across multiple entities side-by-side'). It distinguishes itself from the sibling ai_visibility_check tool by explicitly focusing on multi-entity comparison and ranking, which ai_visibility_check presumably handles a single entity.

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 concrete use case ('competitive AI-marketing audits') and an example question, making it clear when to use this tool. It implicitly names the alternative (ai_visibility_check) as the underlying probe, but does not explicitly state when NOT to use this tool versus alternatives like compare_entities.

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