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

Reveals that it probes each entity with ai_visibility_check, ranks results, and returns specific metrics (score, confidence, signal density), which adds behavioral context beyond the annotation hints. It does not mention potential external API calls or rate limits, but annotations already ensure the operation is safe and read-only.

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 concise sentences that front-load the core purpose and then efficiently add a use case and return-format details. No redundant or extraneous information.

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?

Covers purpose, behavior, use case, and return format, which is essential given no output schema. It relies on the schema for parameter details, which is adequate because the schema already has 100% coverage. A small gap is not explaining interactions with the 'models' parameter, but schema handles that.

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 adds minimal parameter-level meaning, merely restating that the first entity is the subject, a detail already present in the schema. Thus it does not compensate further beyond what the schema provides.

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?

States a specific verb 'Compare' with a clear resource ('AI visibility across multiple entities side-by-side'). It differentiates from the sibling tool ai_visibility_check by emphasizing multi-entity comparison and ranking, making the tool's unique function unmistakable.

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?

Provides an explicit use case ('competitive AI-marketing audits') with a concrete example question. It implies the alternative of using ai_visibility_check for a single entity, but does not explicitly state when not to use this tool or list other alternatives.

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