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

The description discloses internal behavior beyond the annotations: it probes each entity via ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. It also notes that the first entity is treated as the 'subject' for narrative purposes. These details are not present in the annotations (which only indicate read-only/idempotent behavior) and add significant transparency.

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 four sentences, front-loaded with the core purpose, and each sentence provides distinct value (what it does, how it does it, when to use it, what it returns). No wordy or redundant content.

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 there is no output schema, the description compensates by listing the return fields (ranked list, score, confidence, signal density). It also covers internal mechanics, use-case fit, and parameter nuances. Combined with the strong annotations, an agent has enough to use the tool appropriately.

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 meaning beyond the schema by clarifying that the first entity in 'entities' is treated as the subject and the rest are competitors, which affects the narrative output. It also makes the model selection clearer by mentioning free default vs. API key requirement, which the schema already includes but the description reinforces in context.

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, probes each with ai_visibility_check, and ranks them. It distinguishes itself from siblings like ai_visibility_check by focusing on side-by-side comparison and from compare_entities by specifying the AI-marketing use case and output (score, confidence, signal density).

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?

It gives a concrete use case ('competitive AI-marketing audits' with the example 'does Claude know about us as well as our competitors?'), which makes the intended context clear. However, it does not explicitly mention when not to use the tool or name alternative tools (e.g., compare_entities), so it misses the full 'when-not/alternatives' guidance.

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