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

Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, indicating safe and non-modifying behavior. The description adds value by detailing the internal process (calling ai_visibility_check per entity) and output structure (ranked list with score, confidence, signal density). This contextualizes the behavior beyond what annotations alone convey. No contradiction 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 a concise paragraph of four sentences, each adding value: purpose, process, use case, output. It is well-structured and front-loaded with the core function. No redundant or filler sentences, though it could be slightly more terse without losing clarity.

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 has one required parameter, no output schema, and annotations covering safety and idempotency, the description adequately covers what the tool does, how it works, and the nature of the output. It mentions that it ranks and returns score, confidence, signal density. While it doesn't discuss error handling or limits, the scope is sufficiently complete for an agent to understand and use the tool 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?

Input schema has 100% coverage with descriptions for all parameters. The description adds additional context: explaining that the first entity in 'entities' is treated as the 'subject' for narrative, and that omitting 'models' defaults to workers-ai. This extra meaning helps the agent use parameters correctly, justifying a score above the baseline of 3.

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: to compare AI visibility across multiple entities side-by-side. It explains the process (probes each entity with ai_visibility_check, ranks by score) and provides a concrete use case ('does Claude know about us as well as our competitors?'). This effectively distinguishes it from sibling tools like ai_visibility_check (single entity) and compare_entities (potentially different comparison).

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 a clear use case: competitive AI-marketing audits. It implies when to use this tool over alternatives (e.g., when you need multi-entity comparison vs. single-check with ai_visibility_check). However, it does not explicitly state when not to use or list alternative tools, but the context is sufficient for proper selection.

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