create_lookalike_audience

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

With no annotations, the description must fully disclose behavioral traits. It only indicates creation (a write operation) and mentions ratio, but omits details on permissions, side effects, limits, or output format. This is insufficient for an agent to understand the tool's full impact.

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 two sentences, concise and front-loaded with the action. Every sentence contributes meaning without redundancy or fluff, achieving maximum efficiency.

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?

For a simple creation tool with three parameters and no output schema, the description covers the core purpose and key parameter (ratio). However, it does not mention the expected return value (e.g., an audience ID) or any error scenarios, which would improve completeness.

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% with detailed parameter descriptions. The tool description adds value by explaining that the ratio (1-10) determines resemblance closeness, which supplements the schema's lookalike_spec description. This additional context justifies a score above baseline 3.

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 creates a lookalike audience derived from an existing custom audience, with a specific verb and resource. It distinguishes from siblings like create_custom_audience by specifying 'lookalike' and 'based on an existing custom audience'.

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 implies usage for creating lookalike audiences but provides no explicit guidance on when to use this tool vs alternatives (e.g., create_custom_audience) or when not to use it. No exclusions or comparisons are mentioned.

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