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

Annotations already declare readOnlyHint and idempotentHint, covering safety. The description adds meaningful behavioral context by revealing it 'Probes each entity with ai_visibility_check' and that it returns a ranked list with score, confidence, and signal density, which goes beyond what annotations indicate. However, it does not discuss rate limits or the number of probes made, so the bar is not fully met.

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, with three sentences each contributing unique value: the core action and method, a concrete use case, and the output structure. It is front-loaded with the main verb and resource, and contains no redundant or filler content.

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?

With no output schema, the description compensates by clearly summarizing the return format (ranked list with score, confidence, signal density). It covers the tool's purpose, usage context, and methodology. While it could detail the number of probes or error conditions, the combination of detailed schema annotations and a rich description makes it largely complete for the tool's complexity.

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 each parameter already described. The description adds minimal extra meaning, merely echoing the entities behavior (first entry as subject) already present in the schema. Per rubric, baseline of 3 is appropriate when schema carries the semantic load.

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: 'Compare AI visibility across multiple entities side-by-side.' It specifies the method (probes each entity with ai_visibility_check), the ranking output, and the use case example, distinguishing it from single-entity tools like ai_visibility_check and general comparison tools.

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 provides context for use: 'Useful for competitive AI-marketing audits' and an example query. It also names the underlying probe tool (ai_visibility_check), implying single-entity use should go there, but it does not explicitly exclude alternatives or provide a 'when not to use' section, so it stops short of a perfect score.

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