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

The description discloses that it internally calls 'ai_visibility_check' for each entity, ranks results, and returns a ranked list with score, confidence, and signal density. This adds significant value beyond the annotations (readOnlyHint, etc.) which alone don't describe the process.

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?

Three sentences with no filler. First sentence states purpose, second explains mechanism, third gives concrete use case. Every sentence provides essential information.

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 no output schema, the description communicates the return format (ranked list with score, confidence, signal density). It also covers parameters and behavior. Minor gaps: no mention of error handling for API keys or limits on model options, but overall sufficient.

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: it explains that the first entity in the array is treated as the 'subject' for narrative, and clarifies model choices and API key usage for 'anthropic'. This goes beyond the schema descriptions.

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 specific verbs ('Compare', 'Probes', 'ranks', 'surfaces') and clearly states the resource ('AI visibility across multiple entities'). It distinguishes from sibling 'ai_visibility_check' by emphasizing side-by-side comparison and ranking.

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 for use ('competitive AI-marketing audits') with an example question. Although it doesn't explicitly state when not to use, the sibling tools include 'ai_visibility_check' for single-entity checks, implying alternatives.

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