MCP Directory

How to add Facebook Ads Library MCP Server to Cursor

Search and analyze any brand's public Facebook ads — images, videos, messaging, and competitor strategy. Paste the config into ~/.cursor/mcp.json and restart Cursor.

Last updated June 14, 2026 · 243★ · stdio · apikey

Cursor config for Facebook Ads Library MCP Server

git clone https://github.com/proxy-intell/facebook-ads-library-mcp.git && cd facebook-ads-library-mcp && ./install.sh
{
  "mcpServers": {
    "facebook-ads-library-mcp-server": {
      "command": "{{PATH_TO_PROJECT}}/venv/bin/python",
      "args": [
        "{{PATH_TO_PROJECT}}/mcp_server.py"
      ]
    }
  }
}

Setup steps

  1. 1Open Cursor → Settings → MCP → Add new MCP server (or edit ~/.cursor/mcp.json directly).
  2. 2Paste the Facebook Ads Library MCP Server config below into the "mcpServers" object.
  3. 3Fill in placeholder secrets, then save.
  4. 4Cursor reloads MCP servers automatically — check Settings → MCP for a green status dot.
  5. 5Ask Cursor to use one of Facebook Ads Library MCP Server's tools to confirm it's connected.

Before you start

  • Python 3.12+
  • pip (Python package manager)
  • Claude Desktop or Cursor
  • A ScrapeCreators API key (SCRAPECREATORS_API_KEY) for ads data
  • Optional: a Google Gemini API key (GEMINI_API_KEY) for video analysis

What Facebook Ads Library MCP Server can do in Cursor

get_meta_platform_id

Returns the Meta platform ID given one or many brand names (supports multiple brands in a batch).

get_meta_ads

Retrieves ads for specific page(s) by platform ID (supports multiple platform IDs in a batch).

analyze_ad_image

Analyzes ad images for visual elements, text, colors, and composition (enhanced caching).

analyze_ad_video

Analyzes a single ad video using Gemini AI for comprehensive insights (enhanced caching).

analyze_ad_videos_batch

Analyzes multiple videos in a single API call for token efficiency (~88% token savings).

get_cache_stats

Gets statistics about cached media (images and videos) and storage usage.

search_cached_media

Searches previously analyzed media by brand, colors, people, or media type.

cleanup_media_cache

Cleans up old cached media files to free disk space.

Security

Requires a ScrapeCreators API key (SCRAPECREATORS_API_KEY) and, optionally for video analysis, a Google Gemini API key (GEMINI_API_KEY). Keys are stored in a local .env file in the project root and loaded automatically at runtime — they are not passed on the command line. Only public Facebook Ads Library data is queried.

Facebook Ads Library MCP Server + Cursor FAQ

Where is the Cursor config file?

Cursor reads MCP servers from ~/.cursor/mcp.json. Paste the Facebook Ads Library MCP Server config there under the "mcpServers" key and restart the client.

Is Facebook Ads Library MCP Server safe to use with Cursor?

Requires a ScrapeCreators API key (SCRAPECREATORS_API_KEY) and, optionally for video analysis, a Google Gemini API key (GEMINI_API_KEY). Keys are stored in a local .env file in the project root and loaded automatically at runtime — they are not passed on the command line. Only public Facebook Ads Library data is queried.

Do I need to self-host?

No. Proxy (useproxy.dev) offers a fully hosted version of this MCP that works out of the box in ChatGPT, Claude, Manus, and other MCP clients with nothing to install or configure. Self-hosting via the steps above is optional.

What API keys do I need?

A ScrapeCreators API key (SCRAPECREATORS_API_KEY) is required for ads data. A Google Gemini API key (GEMINI_API_KEY) is optional and only needed for video ad analysis. Both are stored in a local .env file.

Which clients are supported for self-hosting?

The README documents an mcpServers config block for both Claude Desktop and Cursor; the server runs over stdio using the project's virtualenv Python.

View repo Full Facebook Ads Library MCP Server page