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

With no annotations, the description fully discloses behavior: dataStatus=no_data means unmeasured not zero, null semantics for citationPresence/shareOfVoice, exclusion of inactive historical data, and the advertising vs organic distinction. This is comprehensive behavioral transparency.

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 dense but every sentence conveys essential information for correct usage and interpretation. It is front-loaded with the main purpose and then details key constraints, though it could be broken into clearer sections for readability.

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 complexity of the tool and no output schema, the description covers critical edge cases: data status, null values, percentage ranges, historical data handling, and the advertising/organic separation. It lacks explicit mention of the return object structure, but provides sufficient interpretive guidance for an agent.

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%, so parameters are already documented. The description adds value by clarifying windowDays default, includeHistoricalPlatforms default and when to enable it, and the meaning of platform filter values. This goes beyond the schema's basic descriptions.

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 retrieves AI search visibility summary metrics for a project, and adds specifics like active platforms and measurement metadata. It differentiates itself from siblings like get_visibility_response by focusing on summary-level metrics and field semantics.

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?

It provides explicit guidance on when to use measurement.reportablePlatforms, warns against combining advertising with organic Presence, and explains the includeHistoricalPlatforms parameter usage. This gives clear direction on correct invocation and interpretation.

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