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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds valuable behavioral context: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. It also discloses return fields (score, confidence, signal density), which is useful beyond 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 succinct sentences, front-loaded with the primary action. Every sentence earns its place: purpose, operation, use case, and output. There is no fluff or repetition.

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 a tool with 4 parameters, no output schema, and moderate complexity, the description covers the core aspects: what it does, how it works (probe, rank, surface), when to use it, and what it returns. The lack of an output schema is compensated by explicitly listing returned fields (score, confidence, signal density).

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 does not add significant meaning beyond the schema—it references 'your brand + N competitors' but the schema already explains first entry as subject. No additional parameter semantics are 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 purpose with a specific verb and resource: 'Compare AI visibility across multiple entities side-by-side.' It distinguishes itself from sibling ai_visibility_check by focusing on multi-entity comparison and ranking, and from compare_entities by mentioning AI visibility scoring.

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 context for when to use the tool, explicitly stating it is 'Useful for competitive AI-marketing audits' and giving an example query. However, it does not explicitly mention when not to use it or provide alternatives, though it implicitly contrasts with ai_visibility_check by describing probing behavior.

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