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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses that the tool 'Probes each entity... with ai_visibility_check', implying multiple network calls and potential rate-limit/cost implications. It also describes the return structure (ranked list with score, confidence, signal density). This adds meaningful behavioral context not present in 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, with the primary purpose front-loaded. Every sentence earns its place: the first states the core function, the second explains mechanics and output, the third gives a use case and example. No fluff 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?

The tool is moderately complex (multi-entity, optional models/API key, no output schema). The description covers the input semantics, the probing behavior, and the return fields (score, confidence, signal density). It does not explain default model behavior or failure modes, but these are partially covered by the input schema. Given the complexity and available annotations, the description is nearly complete.

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%, so the baseline is 3. The description adds extra semantics beyond the schema: 'First entry treated as the "subject" for narrative; rest are competitors.' This clarifies the role of the entities array, which is not stated in the schema. It also reinforces the models/_apiKey relationship, adding value over the structured definitions.

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 resource (multiple entities), the verb (compare), and the outcome (ranked by score, surfaces most/least recognized). It distinguishes itself from siblings like ai_visibility_check (single-entity check) and compare_entities (generic comparison) by explicitly mentioning it probes with ai_visibility_check and ranks results.

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 clear usage context: 'Useful for competitive AI-marketing audits' and gives an example prompt. It implies this is for multi-entity comparison vs. a single check, but doesn't explicitly state when not to use it or name alternative tools. The guidance is clear enough for an agent to select this tool for competitive audits, though exclusions are absent.

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