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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds behavioral detail beyond annotations: it 'Probes each entity with ai_visibility_check, ranks by score' and returns 'ranked list with score, confidence, signal density per entity'. This informs the user about the internal mechanics and output shape, which is valuable. It does not mention potential latency or multi-call effects, but given strong annotations, this is sufficient.

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, tight and front-loaded. It states the core action, the method, a use case, and the return format with no wasted words. Every sentence earns its place.

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 the tool's moderate complexity (multi-entity comparison, dynamic model selection) and the absence of an output schema, the description adequately covers the return value ('ranked list with score, confidence, signal density per entity'). It mentions the key constraint that the first entity is the subject, which is also in the schema. It could add notes about failure modes or rate limits, but it is sufficiently complete for an agent to use effectively.

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% with detailed descriptions for all four parameters. The description adds no new parameter semantics beyond what the schema already provides, such as the entities array's first entry being the subject. It echoes the concept but does not enrich it. Thus 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 tool's function: 'Compare AI visibility across multiple entities side-by-side' and specifies it 'Probes each entity with ai_visibility_check, ranks by score'. This distinguishes it from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison). The purpose is specific and actionable.

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 a clear use case: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"'. It implies this is for multi-entity comparison versus the single-entity ai_visibility_check. It does not explicitly name alternatives or exclusions, but the context is clear enough for a user to know when to invoke it.

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