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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds behavioral context: it probes each entity with ai_visibility_check, ranks results, 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. No contradictions with 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 two sentences long, no wasted words. The first sentence immediately states the core action and benefit. Every sentence adds value and is front-loaded.

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 no output schema, the description adequately covers what the tool returns (ranked list with score, confidence, signal density). It explains the workflow, how the context parameter is used, and the subject role of the first entity. Annotations fill in safety and idempotency. The description is complete for the tool's complexity.

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 baseline is 3. The description adds meaning beyond the schema: it explains that the first entity in the array is treated as the 'subject' for narrative, and describes the output format (ranked list with score, confidence, signal density). It also clarifies that the context parameter is shared across probes for disambiguation.

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 purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies the action (probing each entity with ai_visibility_check), the resource (entities), and the output (ranked list with scores). It distinguishes itself from the sibling ai_visibility_check by being a comparative wrapper.

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 states when to use: 'Useful for competitive AI-marketing audits' and provides an example question. It implies that for single entities, one should use ai_visibility_check, but does not explicitly say when not to use this tool. That omission prevents a score of 5.

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