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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint: false), the description discloses that the tool probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also specifies the return structure (ranked list with score, confidence, signal density). This adds meaningful behavioral context about the tool's internal multi-probe aggregation behavior and output format.

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. It opens with the core function, then explains the mechanism, offers a practical use case, and states the return format. Every sentence earns its place with no redundancy 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?

Given the tool's moderate complexity (multi-probe comparison), the description is complete. It explains the process, the use case, and—since there is no output schema—explicitly lists the return fields (score, confidence, signal density). The annotations and schema cover the remaining safety and parameter details, making the description sufficiently complete for an agent to select and invoke the tool 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 description coverage is 100%, so the baseline is 3. The description does not add significant parameter-level detail beyond the schema; it references entities and competitors in prose but the schema already fully documents each parameter including the 'first entry as subject' semantics. No additional parameter meaning is provided.

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 function: comparing AI visibility across multiple entities side-by-side. It uses a specific verb ('Compare') and resource ('AI visibility across multiple entities'). It also distinguishes itself from likely siblings like ai_visibility_check (single entity) and compare_models (comparing models) by emphasizing multi-entity comparison 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 provides a clear use case ('competitive AI-marketing audits') and gives an example question. It implies when to use it (when comparing multiple entities) but does not explicitly mention alternatives or when not to use it. The context is clear and actionable, though it lacks exclusions or direct contrasts with sibling tools.

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