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

With annotations already declaring readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, the description adds significant behavioral context beyond these: it reveals the tool probes each entity via ai_visibility_check, ranks results by score, surfaces the most/least recognized, and returns a ranked list with score, confidence, and signal density. This gives the agent a clear mental model of the tool's execution and output, going well beyond the 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 compact and front-loaded, with the primary purpose in the first sentence. It efficiently covers mechanism, use case, and return format in four short sentences, with zero filler or redundancy. Every sentence earns its place, making it easy for an agent to quickly parse the essential information.

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 4 parameters, no output schema, and strong annotations, the description is remarkably complete. It explains what the tool does, how it works (probing with ai_visibility_check), what results it returns (ranked list with score, confidence, signal density), and provides a concrete use case. The only details not in the description are in the schema (e.g., entity count limits, _apiKey requirement), so the description fully satisfies the contextual needs.

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 substantial parameter semantics beyond the schema, though it clarifies the expected entity composition ('your brand + N competitors') which aligns with the schema's note about the first entry being the subject. Overall, the schema already carries the parameter details, and the description adds minimal extra value here.

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 a specific verb and resource: '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 explicitly focusing on AI visibility and mentioning the internal mechanism of probing with ai_visibility_check. The use case 'does Claude know about us as well as our competitors?' adds concrete context.

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 this tool ('Useful for competitive AI-marketing audits') and implies it is for multi-entity comparisons, contrasting with ai_visibility_check which likely handles single entities. However, it does not explicitly name an alternative or state when not to use it, leaving some room for inference. The example query effectively communicates the intended scenario.

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