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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds that it calls ai_visibility_check multiple times, treats the first entity as the subject for narrative, and returns a ranked list with score, confidence, and signal density. This adds valuable behavioral context beyond the 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 a single well-structured paragraph that starts with the core action, explains the process, gives a concrete use case, and states the output. Every sentence adds value with no redundancy or unnecessary details.

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?

The description explains the tool's purpose, process, and output format (ranked list with score, confidence, signal density). Given 4 parameters and no output schema, it provides sufficient context for an agent to use the tool. It does not cover edge cases like invalid entities or probe failures, but annotations (idempotent, read-only) mitigate risk.

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 schema already documents all parameters. The description adds minimal additional meaning beyond the schema (e.g., mentioning that entities are probed with ai_visibility_check). Achieves baseline adequacy but does not significantly enhance 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 compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides a concrete use case (competitive AI-marketing audits) and differentiates from siblings like ai_visibility_check (single entity) and compare_entities (more general).

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 explicitly says it is useful for competitive AI-marketing audits and implies when to use (comparing multiple entities). It mentions probing each entity with ai_visibility_check, indirectly indicating that for a single entity, that tool should be used. However, it does not explicitly state when not to use or list alternatives, but the context from sibling tools is clear.

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