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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context: it invokes ai_visibility_check per entity, ranks results, and returns a specific structure (score, confidence, signal density). This goes beyond the annotations without contradicting them.

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?

Two sentences, front-loaded with the main purpose. The first sentence states the action and process; the second provides the use case and return format. Every word earns its place, and there is no redundancy with the schema.

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 tool has 4 parameters and no output schema, but the description compensates by specifying the output fields (score, confidence, signal density) and the invocation workflow. It covers the core behavior and use case sufficiently; minor details like error handling are absent but not critical here.

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, setting a baseline of 3. The description adds extra semantic value by explaining that the first entity is treated as the 'subject' for narrative, which is not in the schema. This justifies a score above baseline.

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 a specific action: 'Compare AI visibility across multiple entities side-by-side.' It details the process (probes each entity with ai_visibility_check, ranks by score) and the result (surfaces most/least recognized). This 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?

The description gives a concrete use case ('competitive AI-marketing audits') and an example question. It implicitly differentiates from the single-entity sibling by emphasizing 'multiple entities side-by-side,' but it does not explicitly state when not to use it or name alternative tools for exclusion.

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