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

Annotations already declare read-only, idempotent, and non-destructive. The description adds value by explaining that it probes each entity via ai_visibility_check, ranks results, and returns confidence and signal density. It also discloses that including 'anthropic' in models requires an _apiKey and makes external API calls, which is beyond the schema's plain field descriptions.

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 and well-structured: three sentences that front-load the purpose, then dive into process and use case. Every sentence adds information with no redundant fluff or filler.

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?

Although there is no output schema, the description specifies the return format (ranked list with score, confidence, signal density per entity). It also covers the default model behavior and optional key requirement, making the tool self-contained for an agent to invoke correctly.

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%, providing a baseline of 3. The description adds semantic value by stating that the first entity is treated as the 'subject' for narrative and the rest are competitors, and that context is shared across all probes. These details are not present in the schema definitions.

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 specifies exactly what the tool does: 'Compare AI visibility across multiple entities side-by-side' and details the process (probes each entity with ai_visibility_check, ranks by score). This clearly distinguishes it from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison).

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?

It provides an explicit use case ('competitive AI-marketing audits') with a concrete example, and notes that it internally uses ai_visibility_check, implying when to use this instead of calling the sibling directly. It does not give explicit exclusions, but the context is clear and actionable.

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