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meta_ads_audiences_create_lookalike

Generate a lookalike audience from an existing source audience in Meta Ads. Specify target country and similarity ratio (0.01–0.20) to control match tightness. Optionally set starting_ratio to exclude the top similarity slice.

Instructions

Creates a Lookalike Audience from an existing source audience. Returns the new audience_id. Mutating, reversible via rollback_apply. Lookalikes typically populate within 24–72h; the approximate_count remains 0 until Meta finishes the similarity build. ratio=0.01 gives the top 1% most similar users in the target country (smallest, highest match); ratio=0.10 gives top 10% (larger reach, looser match). For the base audience list use meta_ads_audiences_list.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_idYesMeta Ads account ID in the format 'act_XXXXXXXXXX' (e.g. 'act_1234567890'). Optional — falls back to META_ADS_ACCOUNT_ID from the configured credentials. The leading 'act_' prefix is required.
nameYesAudience name shown in Ads Manager. Must be unique within the account.
source_audience_idYesSource Custom Audience to build the lookalike from. Meta recommends a source of at least 1,000–10,000 users for good match quality.
countryYesTarget country ISO code(s) for the lookalike expansion. Accepts a single code string (e.g. 'JP') or a list (e.g. ['JP', 'KR']). Lookalike reach is always scoped to the specified country/countries.
ratioYesSimilarity ratio — fraction of the target country's population to include. 0.01 = top 1% (tightest match, smallest audience); 0.20 = top 20% (loosest, largest). Meta caps at 0.20.
starting_ratioNoLower bound of the ratio range. Default 0.0. Advanced: set > 0 to carve out a tiered lookalike that excludes the top-similarity slice (e.g. starting_ratio=0.01, ratio=0.05 = users ranked 1–5% in similarity, excluding the top 1%).
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that the operation is mutating but reversible via rollback_apply, states the population timeframe (24-72h), and explains that approximate_count remains 0 until completion. This goes beyond a simple create statement.

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 description is five sentences, front-loaded with the core action and output. Every sentence adds value: action, mutability/reversibility, timing, ratio semantics, sibling reference. Minor wordiness could be trimmed but overall efficient.

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?

Given the absence of output schema, the description adequately covers return value (audience_id), behavioral traits, timing, and parameter nuances. For a creation tool, this provides sufficient context for correct invocation.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds significant meaning by explaining the ratio parameter (0.01 = top 1%, smallest, highest match) and the starting_ratio parameter for tiered lookalikes. This enhances understanding beyond the schema.

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

Purpose5/5

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

The description uses a specific verb ('Creates a Lookalike Audience') and identifies the resource (existing source audience) and output (new audience_id). It clearly distinguishes from sibling tools like meta_ads_audiences_list, as noted in the last sentence.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool (creating lookalike audiences) and explicitly names an alternative (meta_ads_audiences_list for base audience list). It does not include explicit when-not-to-use scenarios, but the guidance is strong.

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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