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

With annotations already declaring the tool as read-only, idempotent, and non-destructive, the description adds meaningful behavioral context: it probes each entity with ai_visibility_check, ranks by score, surfaces most/least recognized, and returns a ranked list with score, confidence, and signal density. This goes beyond the annotations and gives insight into the tool's internal methodology and output, though it doesn't disclose potential rate limits or error 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, front-loaded with the core action, and includes a concrete example. Every sentence adds value—stating the function, the underlying mechanism, a use case, and the return format—with no redundancy or filler.

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?

Despite having no output schema, the description covers the return format (ranked list with score, confidence, signal density). Combined with the schema's full parameter documentation and annotations covering safety, the description provides a complete picture for an agent to invoke the tool correctly. It doesn't mention edge cases like rate limits or failure handling, but these are less critical given the tool's read-only nature and the provided context.

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%, with detailed parameter descriptions (e.g., 'First entry treated as the subject...' for entities). The description adds minor semantic reinforcement by framing entities as 'your brand + N competitors' and mentioning the probe mechanism, but it doesn't significantly augment what the schema already provides. Baseline 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, with a specific verb ('Compare') and resource ('AI visibility across multiple entities'). It distinguishes itself from the sibling ai_visibility_check by explicitly saying it probes each entity with that tool and ranks results, making the purpose unmistakable.

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 an example query, effectively conveying when to use the tool. It indirectly references the alternative ai_visibility_check by explaining this tool probes each entity with it, but it doesn't explicitly state when not to use it or name other alternatives like compare_entities, so it falls short of a 5.

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