Meta Ads MCP

estimate_audience_size

Estimate potential audience size for Meta Ads campaigns using targeting specifications like demographics, geography, and interests to validate reach before launching.

Instructions

Estimate audience size for targeting specifications using Meta's delivery_estimate API.

This function provides comprehensive audience estimation for complex targeting combinations
including demographics, geography, interests, and behaviors. It also maintains backwards
compatibility for simple interest validation.

Args:
    access_token: Meta API access token (optional - will use cached token if not provided)
    account_id: Meta Ads account ID (format: act_XXXXXXXXX) - required for comprehensive estimation
    targeting: Complete targeting specification including demographics, geography, interests, etc.
              Example: {
                  "age_min": 25,
                  "age_max": 65,
                  "geo_locations": {"countries": ["PL"]},
                  "flexible_spec": [
                      {"interests": [{"id": "6003371567474"}]},
                      {"interests": [{"id": "6003462346642"}]}
                  ]
              }
    optimization_goal: Optimization goal for estimation (default: "REACH"). 
                      Options: "REACH", "LINK_CLICKS", "IMPRESSIONS", "CONVERSIONS", etc.
    interest_list: [DEPRECATED - for backwards compatibility] List of interest names to validate
    interest_fbid_list: [DEPRECATED - for backwards compatibility] List of interest IDs to validate

Returns:
    JSON string with audience estimation results including estimated_audience_size,
    reach_estimate, and targeting validation

Input Schema

TableJSON Schema

Name	Required	Default
`access_token`	No
`account_id`	No
`targeting`	No
`optimization_goal`	No	REACH
`interest_list`	No
`interest_fbid_list`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 7, 2026

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the tool's dual functionality (comprehensive estimation + backwards compatibility) and mentions token caching behavior. However, it doesn't disclose rate limits, authentication requirements beyond the token parameter, error conditions, or whether this is a read-only vs. write operation.

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

Conciseness4/5

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

The description is well-structured with clear sections (purpose, args, returns) and efficiently conveys necessary information. While comprehensive, some sentences could be more concise (e.g., 'It also maintains backwards compatibility for simple interest validation' could be simplified). Overall, most content earns its place.

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 (6 parameters, no annotations, 0% schema coverage), the description does well by explaining parameters thoroughly and mentioning the return format. However, with an output schema present, it could have been more concise about returns. The description adequately covers the tool's purpose and usage but could benefit from more behavioral context.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by providing detailed parameter explanations. It clarifies optional vs. required parameters, provides format examples (account_id format), shows a comprehensive targeting example, lists optimization goal options, and explicitly marks deprecated parameters. This adds substantial value beyond the bare 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 the specific verb ('estimate') and resource ('audience size') with the exact API used ('Meta's delivery_estimate API'). It distinguishes from siblings by focusing on audience estimation rather than creation, fetching, or updating operations found in other tools.

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

Usage Guidelines4/5

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

The description provides clear context about when to use it ('for targeting specifications') and mentions backwards compatibility for simple interest validation. However, it doesn't explicitly state when NOT to use it or name specific alternative tools for related tasks like searching interests or demographics.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.