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meta_ads_split_tests_create

Launch a Meta Ads split test comparing existing ad sets on selected objectives to determine the winning variant with statistical confidence.

Instructions

Creates a new Split Test. Returns the new study_id. Mutating — not automatically reversible; record before-state with mureo_state_action_log_append if you may need to roll back. Meta runs the test for the configured duration, then compares cells on the chosen objective (COST_PER_RESULT / CONVERSIONS / REACH / CPC / CPM). Cells must reference pre-existing ad sets; this tool does not create ad sets. For test analysis post-conclusion use meta_ads_split_tests_get.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesTest name shown in Experiments. Should describe the hypothesis being tested.
cellsYesTest cells (2 or more). Each cell has {name, adsets: [ad_set_id, ...]}. Meta splits traffic evenly across cells.
reasonNoWhy this change is being made: one or two sentences naming the evidence and the expected effect. Stored in the journal and on the action_log entry this call produces, for the operator and the next session.
end_timeYesTest end in ISO 8601. Meta requires at least 4 days between start_time and end_time for statistical significance.
account_idNoMeta 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.
objectivesYesMetrics Meta will use to rank cells. Each entry is {type: COST_PER_RESULT | CONVERSIONS | REACH | CPC | CPM}. Multiple objectives produce multi-dimensional results.
start_timeYesTest start in ISO 8601 (e.g. '2026-04-25T00:00:00+0900'). Must be in the future when the test is created.
descriptionNoFree-text description of the hypothesis. Internal — not shown to end users.
confidence_levelNoStatistical confidence threshold for declaring a winner. Default 95 (95%). Higher values need more spend / longer duration to conclude.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.20.0
    • addedInput schema / properties / reason
      Added value: +{
+  "description": "Why this change is being made: one or two sentences naming the evidence and the expected effect. Stored in the journal and on the action_log entry this call produces, for the operator and the next session.",
+  "maxLength": 500,
+  "type": "string"
+}
  2. Changed1 schema field changedv0.10.37
    • addedInput schema / additionalProperties
      Added value: +false
  3. Changed1 schema field changedv1.0.7
    • changedInput schema / required
      Previous value: -[
-  "account_id",
-  "name",
-  "cells",
-  "objectives",
-  "start_time",
-  "end_time"
-]New value: +[
+  "name",
+  "cells",
+  "objectives",
+  "start_time",
+  "end_time"
+]
  4. Addedv0.9.12
  5. Removedv0.9.6
  6. Addedv0.9.2
  7. Removedv0.9.1
  8. Addedv1.0.5

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It explicitly states the tool is mutating, not automatically reversible, recommends mureo_state_action_log_append for rollback, explains that Meta runs the test for the configured duration and compares cells on selected objectives, and clarifies that ad sets must already exist. This is strong transparency for a mutating tool.

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?

The description is compact, front-loaded with the core purpose and return value, and every sentence adds meaningful information: mutation warning, rollback guidance, duration behavior, prerequisite on ad sets, and sibling routing. There is no filler or redundancy.

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

Completeness5/5

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

Given the tool has no annotations and no output schema, the description covers all critical operational context: what it creates, what it returns, side effects, prerequisites, and where to go afterward. The parameter details are already fully documented in the schema, so nothing needed for correct invocation is missing.

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 coverage is 100%, which sets a baseline of 3. The description adds value beyond the schema by highlighting that cells must reference pre-existing ad sets, that the tool does not create ad sets, and that Meta compares cells on the chosen objectives. These are important behavioral constraints on parameters like cells and objectives that the schema alone only partially conveys.

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 clearly states the action ('Creates a new Split Test'), the resource, and the key return value ('Returns the new study_id'). It also differentiates from related sibling tools by explicitly noting that it does not create ad sets and by directing post-conclusion analysis to meta_ads_split_tests_get.

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

Usage Guidelines5/5

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

The description gives explicit usage context: it is for creating split tests, cells must reference pre-existing ad sets, and analysis after the test concludes should use meta_ads_split_tests_get. It also warns about mutation and rollback, helping the agent decide when to use this tool versus logging state or other Meta ad management tools.

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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