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

With annotations declaring readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, the safety profile is already clear. The description adds behavioral context beyond annotations: it probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density. This gives the agent a transparent understanding of the tool's process and output without contradicting 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 three sentences, front-loaded with the core purpose. Each sentence earns its place: the first states what it does, the second explains the mechanism and ranking, and the third provides a concrete use case and return summary. There is no fluff or repetition.

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 (4 parameters, no output schema), the description is complete: it explains the core functionality, the process (probing and ranking), the return value (ranked list with score/confidence/signal density), and provides a practical usage scenario. The absence of an output schema is compensated by describing the response format. This is sufficient for an agent to select and invoke the tool correctly.

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 input schema has 100% description coverage for all 4 parameters, so the baseline is 3. The description adds a little context by framing entities as 'your brand + N competitors' and mentioning the 'subject' concept, but this largely mirrors the schema's description of the first entity as the subject. No additional meaning beyond the schema is provided.

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 'Compare AI visibility across multiple entities side-by-side', which is a specific verb+resource+scope. It goes on to explain the mechanism (probes with ai_visibility_check, ranks by score) and distinguishes itself from the sibling ai_visibility_check (which likely handles a single entity) by focusing on multiple entities and side-by-side 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 provides clear context for when to use the tool: 'Useful for competitive AI-marketing audits' with an example question. It implies that for a single entity check, one might use ai_visibility_check instead, but it does not explicitly state exclusions or name alternative tools beyond the implicit contrast. This is a clear context with no exclusions, so 4.

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