MCP Server

Connect AI assistants to the AudienceLab API via the Model Context Protocol

Overview

AudienceLab provides a remote Model Context Protocol (MCP) server that lets AI assistants interact with the AudienceLab API. Create audiences, enrich contacts, manage pixels, and more — all through natural language.

Curious how this MCP server was built? It’s generated from our OpenAPI spec — check out openapi-to-mcp on GitHub to see how it works under the hood.

Quick Setup

Add the following to your MCP client configuration:

Claude Code

claude mcp add audiencelab --transport http https://mcp.audiencelab.io/mcp --header "X-Api-Key: YOUR_API_KEY"

Replace YOUR_API_KEY with your AudienceLab API key.

stdio-only Clients

For MCP clients that only support stdio transport, use mcp-remote as a proxy:

{
  "mcpServers": {
    "audiencelab": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.audiencelab.io/mcp",
        "--header",
        "X-Api-Key:YOUR_API_KEY"
      ]
    }
  }
}

Available Tools

The MCP server exposes all AudienceLab API endpoints as tools:

Tool	Description
create_audience	Create an audience with filters
get_audiences	List all audiences
audience_lookup	Get audience data by ID
preview_audience	Preview audience count before creating
create_custom_audience	Create a custom intent audience
audience_attributes	Get available filter attribute values
audience_delete	Delete an audience
enrich_contact	Real-time contact enrichment
create_enrichment_job	Upload records for batch enrichment
get_enrichments	List enrichment jobs
create_pixel	Create a new pixel
get_pixels	List all pixels
pixel_lookup	Get pixel events
pixel_delete	Delete a pixel

Authentication

The MCP server uses your AudienceLab API key for authentication. Pass it in the X-Api-Key header. The server forwards it to the AudienceLab API on each request.

Your API key is never stored on the MCP server.