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Create A/B Test (Ad Study)

meta_create_ad_study

Creates an A/B test to compare campaigns or ad sets. Define test cells, traffic splits, and confidence level.

Instructions

Creates an A/B test (ad study) to compare campaigns or ad sets.

Args:

  • ad_account_id (string): Ad account ID (e.g., act_123456789)

  • name (string): Study name

  • description (string, optional): Study description

  • start_time (string): ISO 8601 start time

  • end_time (string): ISO 8601 end time

  • type (enum): SPLIT_TEST or HOLDOUT

  • cells (array): Test cells, each with name, treatment_percentage, and optional campaign_ids/adset_ids

  • confidence_level (number, default 95): Statistical confidence level (e.g., 90, 95, 99)

Returns: The created study ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ad_account_idYesAd account ID (e.g., act_123456789)
nameYesStudy name
descriptionNoStudy description
start_timeYesISO 8601 start time
end_timeYesISO 8601 end time
typeYesStudy type
cellsYesTest cells (minimum 2)
confidence_levelNoStatistical confidence level
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readablemarkdown
Behavior4/5

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

The description correctly indicates a write operation (creates) and specifies the return value (study ID). With annotations not providing behavioral hints, the description carries the burden, and it adequately discloses the creation action. However, it does not detail side effects or permissions needed.

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

Conciseness3/5

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

The description is front-loaded with a clear purpose sentence, but the subsequent Args section is lengthy and duplicates schema content. It could be more concise by omitting redundant parameter descriptions and focusing on usage notes.

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 tool's complexity (9 parameters, nested cells array), the description covers all required and optional fields and states the return value. It is sufficient for an agent to understand the tool's purpose and parameters, though it could benefit from an example or edge case note.

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 coverage is 100%, so the baseline is 3. The description repeats parameter details from the schema almost verbatim, adding minimal new meaning. It does not provide context on how parameters interact or examples of valid values beyond what the schema offers.

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 clearly states it creates an A/B test (ad study) to compare campaigns or ad sets. The verb 'creates' is specific and the resource 'A/B test (ad study)' is well-defined. While it distinguishes from siblings by naming the specific study type, it does not explicitly highlight what makes this tool unique among other 'create' tools.

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 description implies usage for creating A/B tests but does not provide explicit guidance on when to use this tool versus alternatives like meta_create_campaign or meta_create_adset. It lacks context on prerequisites, best practices, or when not to use it.

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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