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Create Meta Lookalike Audience

create_meta_lookalike_audience

Creates a lookalike audience from a source custom audience. source_audience_id from list_meta_audiences. country ISO alpha-2. ratio 0.01-0.20 (1%-20%; smaller = more similar). confirm=false = preview.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
ratioNo
confirmNo
countryYes
account_idYes
source_audience_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false and idempotentHint=false, so the safety profile is largely covered. The description adds a non-obvious behavioral trait beyond the annotations: the confirm=false dry-run mode, which lets the agent preview without creating anything. Auth and rate-limit details are absent, but the preview mechanic is a genuinely useful disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is a compact, front-loaded set of fragments led by the core purpose. Every clause carries information and nothing is wasted, though the telegraphic style is dense and could benefit from slightly clearer sentence structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be described, and the annotations cover the safety profile. The description supplies the essential workflow (preview/confirm) and the meaning of the key parameters, leaving only minor gaps such as account_id and the duplicate-creation implication of idempotentHint=false.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must carry the burden and does well: it explains source_audience_id origin (list_meta_audiences), country format (ISO alpha-2), ratio's valid range and semantic direction (0.01–0.20, smaller = more similar), and confirm's preview behavior. Only name and account_id are left implicit, which is acceptable for self-evident fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ("Creates a lookalike audience from a source custom audience") that clearly describes the operation. It does not explicitly differentiate from siblings like create_lookalike_audience or create_tiktok_lookalike_audience, but the Meta context and source-custom-audience constraint point the agent in the right direction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The "confirm=false = preview" fragment implies a two-step preview-then-confirm workflow, and "source_audience_id from list_meta_audiences" tells the agent where to source the prerequisite. However, there is no explicit when-not guidance or named alternative, so usage is only implied rather than stated.

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

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