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 that: it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. It also notes the first entity is treated as the 'subject', which is non-obvious and helpful for understanding behavior.

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, with the core action in the first sentence and supporting details (how it works, use case, return value) in subsequent sentences. Every sentence contributes value: no fluff, no repetition of annotations, and information 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 there is no output schema, the description adequately explains the return value ('ranked list with score, confidence, signal density per entity'). It also covers the use case, the number of entities (via schema, but the description implies N competitors), and the subject/competitor distinction. The tool's complexity is well addressed.

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 meaning beyond what the schema already provides for parameters; for example, it mentions 'first entry treated as subject' but this is already in the schema. The description's reference to probing with ai_visibility_check indirectly maps to 'entities' but adds no new parameter detail.

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 uses a specific verb ('Compare') and resource ('AI visibility across multiple entities side-by-side'), clearly distinguishing it from sibling tools like ai_visibility_check (which probes a single entity) and compare_entities (generic comparison). It also names the underlying probe mechanism and the output (ranked list), making the purpose unambiguous.

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 gives clear context: 'Useful for competitive AI-marketing audits' and an example query. It implies comparison across multiple entities, which differentiates it from ai_visibility_check, but does not explicitly state 'use this instead of X when...' or list exclusions. Thus it meets the 'clear context, no exclusions' bar.

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