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

Annotations already declare the tool read-only, open-world, idempotent, and non-destructive. The description adds behavioral context beyond this: it orchestrates multiple ai_visibility_check calls, ranks results, and returns a structured list with score/confidence/signal density. This explains the aggregation behavior and expected output, which annotations do not cover.

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 compact (three sentences) and front-loaded with the primary purpose. It efficiently covers mechanism, use case, and output format without redundant wording or restating annotations. Each sentence earns its place.

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?

For an aggregation tool with no output schema, the description properly discloses the return type (ranked list with score, confidence, signal density) and the process (calls ai_visibility_check per entity). However, it omits potential time/cost implications of multiple probes and the need for an API key when using the 'anthropic' model, though these are captured in the schema. Overall, sufficiently complete for effective use.

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 semantic value, only framing entities as 'your brand + N competitors' and noting the first is the subject — but this is also implied in the schema's entity description. No significant extra parameter-level meaning is provided.

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' and explicitly describes the mechanism (probes each entity with ai_visibility_check, ranks by score). It specifies the scope (your brand + N competitors) and output (most/least recognized), distinguishing it from single-entity sibling ai_visibility_check.

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 provides a clear use case ('competitive AI-marketing audits') and an example query ('does Claude know about us as well as our competitors?'). It implies this tool is for multi-entity comparison while single-entity tools like ai_visibility_check are for individual probes, though it does not explicitly name alternatives or state exclusions.

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