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

The description discloses that the tool internally calls ai_visibility_check for each entity and then ranks by score, which is behavioral context beyond the annotations. It also reveals the output includes a ranked list with score, confidence, and signal density per entity. This adds meaningful transparency about how the tool works, though it does not cover rate limits or permissions, which are adequately addressed by readOnlyHint and other 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 three sentences long, front-loaded with the core action in the first sentence. Each sentence adds distinct value: the first states the purpose, the second explains the process and output, and the third gives a concrete use case. There is no wasted wording or redundancy.

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?

Given the tool's moderate complexity (4 params, no output schema, but strong annotations), the description is very complete. It explains the internal mechanism (probing with ai_visibility_check), the output structure (ranked list with score, confidence, signal density), and a practical use case. The schema covers parameters, and annotations cover safety, so the description fills in the remaining process and return-value details effectively.

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?

The schema provides 100% coverage for all parameters, so the baseline is 3. The description adds no new information about parameters beyond what the schema already includes; it only restates that the first entity is the subject, which is already documented. Therefore, the description provides minimal added value for parameter semantics.

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 side-by-side, using a specific verb and resource. It distinguishes itself from the sibling tool ai_visibility_check by emphasizing multi-entity comparison and mentions ranking by score and surfacing most/least recognized. The example 'does Claude know about us as well as our competitors?' concretely illustrates the purpose.

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 mentions the tool is useful for competitive AI-marketing audits, which provides clear context for when to use it. It implies a comparison use case by stating it probes each entity (brand + N competitors) with ai_visibility_check, subtly differentiating from single-entity alternatives. However, it does not explicitly state exclusions or name alternatives like compare_entities, so it stops short of full when/not guidance.

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