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

The description reveals that the tool probes each entity with ai_visibility_check, ranks results by score, and returns per-entity score, confidence, and signal density. This goes beyond the annotations (readOnlyHint, idempotentHint) and adds meaningful behavioral context. 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 only two sentences, with the primary purpose front-loaded and a helpful example in quotes. Every word contributes value, and there is no redundancy or fluff.

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 explains the return format (ranked list with score, confidence, signal density) and the target use case, while the schema covers parameter constraints like entity count (2-8) and default model. It could mention potential side effects like multiple API calls, but the overall context is sufficiently complete.

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 schema already provides 100% descriptive coverage for all parameters, including the role of the first entity as the 'subject' and the optional nature of models and _apiKey. The description adds minimal additional parameter insight beyond noting that entities are probed, so the baseline score 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 compares AI visibility across multiple entities side-by-side, which is a specific verb+resource action. It distinguishes itself from sibling tools like ai_visibility_check by explicitly mentioning multi-entity comparison, ranking, and identifying most/least recognized entities.

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 ('competitive AI-marketing audits') and a concrete example question ('does Claude know about us as well as our competitors?'). However, it does not explicitly state exclusions or directly compare with alternative tools like ai_visibility_check or compare_entities.

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