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create_lookalike_audience

Create a lookalike audience from an existing custom audience to target new people similar to your source, using a country and 1%-10% ratio to set similarity.

Instructions

Create a lookalike audience based on an existing custom audience. Ratio (1-10) determines how closely the new audience resembles the source.

Input Schema

NameRequiredDescriptionDefault
nameYesLookalike audience name
account_idNoAd account ID to create the audience in (e.g. 'act_123' or '123'). Falls back to META_AD_ACCOUNT_ID env var if omitted.
lookalike_specYesJSON string: {country: 'US', ratio: 0.01-0.10} where ratio is the lookalike percentage (1%-10%)
origin_audience_idYesSource custom audience ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.6.1
    • addedInput schema / properties / account_id
      Added value: +{
      +  "description": "Ad account ID to create the audience in (e.g. 'act_123' or '123'). Falls back to META_AD_ACCOUNT_ID env var if omitted.",
      +  "type": "string"
      +}
  2. First observedv1.2.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It only notes the ratio's effect, omitting permissions, mutation effects, account fallback behavior, and return characteristics. It also says ratio is '1-10' while the schema defines it as 0.01-0.10, creating ambiguity.

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

Conciseness5/5

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

Two concise sentences with the core purpose and key behavioral note front-loaded. No wasted text.

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

Completeness3/5

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

For a create/mutation tool with no annotations and no output schema, the description covers the source audience and ratio but leaves important operational context unexplained, such as account fallback behavior and what happens on success or failure.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all four parameters. The description adds a note about ratio but does not meaningfully document the other parameters and its ratio scale conflicts with the schema's percentage range.

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?

The description gives a specific verb and resource: 'Create a lookalike audience based on an existing custom audience.' It clearly distinguishes this from creating a custom audience, though it does not name the sibling tool explicitly.

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?

Usage is implied: use this when you want to derive a lookalike audience from an existing custom audience. However, there are no explicit when-not conditions, prerequisites, or named alternatives to guide selection.

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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