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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing the tool's safety profile. The description adds behavioral context beyond this: it explains the process (probes each entity, ranks, returns ranked list) and output details (score, confidence, signal density). This provides useful transparency without contradicting 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, front-loaded with the main action, and each sentence earns its place. It avoids fluff and presents information efficiently. The structure is clear and easy to parse.

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?

No output schema is provided, but the description explicitly states the return format: 'ranked list with score, confidence, signal density per entity.' It also explains the input semantics, process, and use case. The description is complete for a tool of moderate complexity, covering all essential aspects without gaps.

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 descriptions for all four parameters. The description adds significant meaning beyond schema: it clarifies that the first entity is treated as the 'subject', explains optional parameters (models, _apiKey, context), and describes the role of context in disambiguation. This provides high value for 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 the tool's purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies the action (probe, rank, surface) and the resource (entities). It distinguishes from sibling tool ai_visibility_check by emphasizing multiple entities and comparison, and the use case for competitive audits.

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 gives clear context: 'Useful for competitive AI-marketing audits' and provides an example question. It implies when to use this tool (for comparison) vs ai_visibility_check (single entity), but it does not explicitly state when not to use it or list alternative tools. The guidance is clear but lacks explicit exclusions.

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