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mathieubernardi

meta-mcp-py

create_custom_audience

Create an empty custom audience for your ad account, choosing between customer list (CUSTOM) or engagement-based (ENGAGEMENT) subtypes, ready to be populated with data.

Instructions

Crée une audience personnalisée vide, prête à être alimentée.

subtype : CUSTOM (liste de clients) ou ENGAGEMENT (interactions).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
subtypeNoCUSTOM
descriptionNo
ad_account_idYes
customer_file_sourceNoUSER_PROVIDED_ONLY

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits on its own. It only states that the audience is created empty and explains the subtype values, but says nothing about required permissions, side effects, idempotency, async behavior, or what the response contains. For a mutation tool, this is a significant transparency gap.

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 two short sentences with no filler. The primary action and resource are front-loaded, and the subtype clarification is concise and directly useful. Every word earns its place.

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?

Given 5 parameters, 0% schema coverage, and no annotations, the description is too thin. It omits the purpose of `customer_file_source`, any prerequisites like ad account permissions, and crucially lacks guidance on how `subtype` interacts with `customer_file_source`. An agent would likely need to guess for a correct call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds meaning only for `subtype` (CUSTOM vs ENGAGEMENT), which is genuinely helpful. However, it does not explain `customer_file_source`, which is obscure, and only indirectly hints at `name`. Most of the 5 parameters remain undocumented in both schema and description.

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 ('Crée' / creates), the resource ('une audience personnalisée vide' / an empty custom audience), and its state ('vide, prête à être alimentée' / empty, ready to be populated). This differentiates it from siblings like create_lookalike_audience and add_users_to_audience, making the tool's specific purpose unmistakable.

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 phrase 'prête à être alimentée' implies this tool is for creating an empty audience that will later be populated, but it does not explicitly say when to use this over create_lookalike_audience or that add_users_to_audience should follow. Usage is implied rather than clearly stated, and no alternative tools are named or contrasted.

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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