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ZLeventer

google-ads-mcp

gads_list_audiences

List remarketing audiences and customer match lists with size, eligibility, and match rate. Identify available user lists for targeting in Google Ads campaigns.

Instructions

List user lists (remarketing audiences, customer match lists) in the account with size, eligibility, and match rate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customer_idNoOverride GOOGLE_ADS_CUSTOMER_ID for this call
limitNo

Implementation Reference

  • The handler function `listAudiences` that executes the tool logic for gads_list_audiences. It queries Google Ads API for user list data (remarketing audiences, customer match lists) with fields like id, name, description, type, membership_status, size_for_display, size_for_search, eligible_for_search, eligible_for_display, and match_rate_percentage.
    export async function listAudiences(args: z.infer<z.ZodObject<typeof listAudiencesSchema>>) {
      const customer = getCustomer(args.customer_id);
      const rows = await customer.query(`
        SELECT
          user_list.id,
          user_list.name,
          user_list.description,
          user_list.type,
          user_list.membership_status,
          user_list.size_for_display,
          user_list.size_for_search,
          user_list.eligible_for_search,
          user_list.eligible_for_display,
          user_list.match_rate_percentage
        FROM user_list
        WHERE user_list.membership_status = 'OPEN'
        ORDER BY user_list.size_for_search DESC
        LIMIT ${args.limit}
      `);
      return { rowCount: rows.length, rows };
    }
  • The input schema `listAudiencesSchema` defining optional customer_id override and a limit (max 10000, default 100) for the gads_list_audiences tool.
    export const listAudiencesSchema = {
      customer_id: z.string().optional().describe("Override GOOGLE_ADS_CUSTOMER_ID for this call"),
      limit: z.number().int().positive().max(10000).default(100),
    };
  • src/index.ts:167-172 (registration)
    Registration of `gads_list_audiences` tool on the MCP server with its description, schema, and handler.
    server.tool(
      "gads_list_audiences",
      "List user lists (remarketing audiences, customer match lists) in the account with size, eligibility, and match rate.",
      listAudiencesSchema,
      async (args) => { try { return ok(await listAudiences(args)); } catch (e) { return err(e); } }
    );
  • Import of `listAudiences` and `listAudiencesSchema` from the audiences module into the main index file.
    import {
      campaignAudienceTargeting,
      campaignAudienceTargetingSchema,
      listAudiences,
      listAudiencesSchema,
    } from "./tools/audiences.js";
Behavior2/5

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

With no annotations, the description should disclose behavioral traits. It only states a read operation with output fields, omitting details on permissions, data freshness, filtering, or side effects. This is minimally transparent.

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?

A single, well-structured sentence that front-loads the action and includes key specifics. Every word adds value, making it efficient and easy to parse.

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

Completeness3/5

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

The description partially covers return fields but lacks pagination details (despite the limit parameter), sorting, or filtering. Without an output schema, more completeness would help, but it's adequate for a simple list tool.

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 coverage is 50%; only customer_id has a description. The tool description adds no parameter semantics beyond the schema, failing to explain the limit parameter or how parameters affect behavior.

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 verb 'List' and the resource 'user lists' with specific examples (remarketing audiences, customer match lists). It also mentions key output fields (size, eligibility, match rate), making the tool's purpose distinct from siblings like gads_list_accounts or gads_list_campaigns.

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 provides no guidance on when to use this tool versus alternatives, such as gads_campaign_audience_targeting. It does not mention prerequisites, exclusions, or recommended contexts, leaving the agent to infer usage.

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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