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

Annotations (readOnly, openWorld, idempotent, non-destructive) already cover safety. The description adds meaningful process details beyond those: it probes each entity, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, and signal density. This explains the operational flow without contradicting 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?

Three concise sentences: action, process, use case. Every sentence carries unique value, no fluff or repetition. The most important information (compare, rank, returns) 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?

The description fully explains the tool's purpose, methodology, use case, and return format (ranked list with score, confidence, signal density). Combined with a comprehensive input schema and safety annotations, the agent has all necessary context to invoke the tool correctly without needing an output schema.

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?

The input schema already provides 100% coverage, including the crucial note that the first entity is treated as the 'subject' and rest are competitors. The description adds only a high-level 'your brand + N competitors' but does not improve on the schema's parameter-level detail. Since the schema fully documents all four parameters, the baseline of 3 is appropriate.

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 specific action: 'Compare AI visibility across multiple entities side-by-side.' It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison) by focusing on AI presence and ranking. The inclusion of 'Probes each entity with ai_visibility_check' and 'ranks by score' makes the tool's function unmistakable.

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 gives a use case: 'Useful for competitive AI-marketing audits.' It also implies when to use (when you need a multi-entity comparison) and effectively contrasts with ai_visibility_check by showing this is the multi-entity variant. However, it does not explicitly name alternatives or say when NOT to use, so it falls short of a full 5.

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