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

The description adds valuable behavioral details beyond the annotations: it reveals the tool internally calls ai_visibility_check per entity, ranks by score, and returns a ranked list with score, confidence, and signal density. This explains observable behavior and output structure, making the tool's execution predictable.

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, each serving a purpose: stating the main function, explaining the mechanism, providing a use case, and specifying outputs. It is front-loaded, dense, and free of unnecessary wording.

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?

Despite having no output schema, the description specifies the return format (ranked list with score, confidence, signal density). The annotation covers safety, and the schema fully documents parameters, so together they give a complete picture for invoking 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?

Since the schema descriptions cover 100% of parameters, the description doesn't need to add much. It reiterates the 'first entry as subject' concept already in the schema and introduces the brand-vs-competitor framing, but adds no new concrete parameter semantics beyond the schema.

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 specifically states the tool compares AI visibility across multiple entities, probes each with ai_visibility_check, and ranks results. It distinguishes itself from the single-entity sibling ai_visibility_check by emphasizing side-by-side comparison and from generic compare_entities by focusing on AI presence.

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 clearly gives a usage context ('competitive AI-marketing audits') and implies when to use it (comparing multiple entities' AI visibility). However, it doesn't explicitly name alternatives or state when not to use it, so it stops short of full exclusion guidance.

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