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meta-ads-mcp

meta-ads-mcp

by mikusnuz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

list_experiments

List A/B test experiments for a Meta ad account to review ad studies, track test results, and manage experiments by account ID.

Instructions

List A/B test experiments (ad studies) for the ad account.

Input Schema

NameRequiredDescriptionDefault
afterNoPagination cursor for next page
limitNoNumber of results to return
fieldsNoComma-separated fields to return
account_idNoAd account ID to query (e.g. 'act_123' or '123'). Falls back to META_AD_ACCOUNT_ID env var if omitted.

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 query (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

A3.8/5.0
Behavior3/5

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

Since no annotations are provided, the description carries the behavioral burden. As a listing tool, it lacks unique behavioral constraints (like write permissions or destructive nature) to disclose. It remains a viable minimum disclosure for a read operation.

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 maximally concise, achieving its purpose in a single, highly focused sentence. There is zero wasted text, and the core function is front-loaded.

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 comprehensive input schema that covers pagination and filtering options, the description is functionally complete. It is a solid definition, though it does not proactively describe limitations or potential data gaps outside of the defined parameters.

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?

The input schema coverage is 100%, meaning the schema already provides full documentation for all parameters. Therefore, the description adds no semantic value that was not already present in the structured input 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 clearly states its function: to list A/B test experiments. By including the parenthetical clarification '(ad studies)', it strongly links the function to the advertising domain, successfully differentiating it from a generic experiment retrieval tool.

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?

It implies scope by stating the experiments are 'for the ad account,' which restricts usage. However, it does not provide explicit guidance on when this tool is the correct choice versus, for example, using get_experiment to retrieve details of a single experiment.

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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