create_lookalike_audience

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

With no annotations, the description must disclose behavioral traits. It only explains the effect of ratio (closeness to source), but fails to mention permissions, return value, whether the operation is synchronous, or any side effects beyond creation. For a mutation tool with zero annotation coverage, this is a significant transparency gap.

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, front-loaded with the main action and key parameter concept. No fluff or redundancy. Every word earns its place.

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?

The tool is a create operation with 3 parameters, no output schema, and no annotations. The description does not mention what the tool returns, any prerequisites (like ad account), or error scenarios. Given the calibration example of update_drive with a similar profile, a score of 2 is appropriate for a mutation tool that leaves critical context undisclosed.

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 the baseline is 3. The description adds meaningful context for the 'ratio' parameter by explaining it determines how closely the new audience resembles the source, which goes beyond the schema's 'lookalike percentage' wording. This extra conceptual explanation justifies a score above baseline.

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 uses a specific verb ('Create') and a specific resource ('lookalike audience'), clearly distinguishing it from sibling tools like create_custom_audience. It also provides a key distinguishing detail (ratio determines closeness). This is exactly what a purpose statement should do.

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 by stating the tool is for creating a lookalike from an existing custom audience, which is clear context. However, it doesn't explicitly state when not to use it or mention alternatives, such as create_custom_audience for non-lookalike audiences. Still, the context is clear and no exclusions are needed.

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