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meta-ads-mcp

meta-ads-mcp

by mikusnuz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

create_experiment

Define and launch new A/B test experiments (ad studies) for your campaigns. Configure start and end times and specify detailed test cells using the Meta Marketing API.

Instructions

Create a new A/B test experiment (ad study).

Input Schema

NameRequiredDescriptionDefault
nameYesExperiment name
typeNoStudy type
cellsYesJSON array of test cells: [{name, campaign_id}]
end_timeYesEnd time in ISO 8601 or Unix timestamp
account_idNoAd account ID to create the experiment in (e.g. 'act_123' or '123'). Falls back to META_AD_ACCOUNT_ID env var if omitted.
start_timeYesStart time in ISO 8601 or Unix timestamp
descriptionNoExperiment description

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 experiment 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

B3.2/5.0
Behavior2/5

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

Since there are no annotations, the description carries the full burden of behavioral disclosure. It does not inform the agent about the side effects, necessary permissions for mutation, or any critical failure behaviors associated with creating a live ad study.

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?

It is a single, highly efficient sentence that front-loads the necessary action and resource name. There is zero waste in the language used.

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

Completeness2/5

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

As a complex, mutation-based operation (creating an experiment), the definition is lacking in completeness. It does not provide necessary context about dependencies, scope, or potential impact beyond the basic definition.

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%, meaning all parameter definitions are handled comprehensively by the structured input schema. As such, the description adds no additional semantic value to the parameters.

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 ('Create') and clearly identifies the resource ('A/B test experiment' or 'ad study'). This verb explicitly distinguishes it from sibling functions like 'list_experiments' or 'get_experiment'.

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

Usage Guidelines2/5

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

The description is purely declarative and offers no guidance on when to use this tool versus alternatives. It fails to specify prerequisites or constraints, such as when an existing experiment must be updated instead of created.

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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