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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds meaningful behavioral detail: it probes each entity via ai_visibility_check, ranks by score, and returns a list with score, confidence, and signal density. This gives insight into internal mechanics without contradicting any annotation.

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 concise (four sentences) and front-loaded with the primary purpose. Each sentence adds value: purpose, mechanism, example use case, and output format. No redundant or filler content.

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 complexity (4 params, no output schema), the description provides a solid overview including return format and use case. It does not mention edge cases or error handling, but the complete schema and clear purpose make it sufficiently complete for an agent to understand what the tool does and what it returns.

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 coverage is 100% for all 4 parameters, so the baseline is 3. The description does not add significant meaning beyond the schema; it restates the first-entity-as-subject concept but otherwise relies on the schema's thorough documentation.

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 function: 'Compare AI visibility across multiple entities side-by-side,' with a specific verb and resource. It distinguishes from the sibling ai_visibility_check by explicitly mentioning it probes each entity with that sub-tool and ranks results.

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?

Provides clear usage context: 'Useful for competitive AI-marketing audits' with an example query. However, it does not explicitly state when NOT to use it or name alternative tools for single-entity checks, though the mention of ai_visibility_check implies the distinction.

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