Facebook Ads Library MCP Server

This is a Model Context Protocol (MCP) server for the Facebook Ads Library.

With this you can search Facebook's public ads library for any company or brand, see what they're currently running and analyze their advertising. You can analyze ad images/text, analyze video ads with comprehensive insights, compare companies' strategies, and get insights into what's working in their campaigns.

✨ New Features

🚀 Batch Processing: Query multiple brands and platform IDs simultaneously to reduce API calls and improve efficiency
🎬 Video Batch Analysis: Analyze multiple videos in a single Gemini API call with ~88% token savings
💳 Smart Credit Management: Automatic detection of API credit exhaustion with direct links to top-up your ScrapeCreators account
⚡ Enhanced Performance: Intelligent caching and request deduplication for faster results
🔄 Backward Compatible: All existing queries work unchanged while gaining new batch capabilities

Here's an example of what you can do when it's connected to Claude.

https://github.com/user-attachments/assets/a47aa689-e89d-4d4b-9df7-6eb3a81937ee

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Example Prompts

Single Brand Analysis

How many ads is 'AnthropicAI' running? What's their split across video and image?

What messaging is 'AnthropicAI' running right now in their ads?

Analyze the video ads from 'Nike' and extract their visual storytelling strategy, pacing, and brand messaging techniques.

Batch Analysis (New!)

Compare the current advertising strategies across Nike, Adidas, and Under Armour. Show me their ad volumes, messaging themes, and creative approaches.

Do a deep comparison to the messaging between 'AnthropicAI', 'Perplexity AI' and 'OpenAI'. Give it a nice forwardable summary.

Analyze the holiday campaign strategies for Coca-Cola, Pepsi, Dr Pepper, and Sprite. What themes are they using?

Get the current ads for all major streaming services: Netflix, Disney+, Hulu, HBO Max, Amazon Prime Video, and Apple TV+. Compare their positioning strategies.

Installation

Prerequisites

Python 3.12+
Anthropic Claude Desktop app (or Cursor)
Pip (Python package manager), install with python -m pip install
An access token for Scrape Creators
A Google Gemini API key for video analysis (optional, only needed for video ads)

Quick Install (Recommended)

Clone and run the install script

git clone http://github.com/talknerdytome-labs/facebook-ads-library-mcp.git
cd facebook-ads-library-mcp

# For macOS/Linux:
./install.sh

# For Windows:
install.bat

The install script will:

Create a virtual environment for dependency isolation
Install all required dependencies
Set up your configuration files

Configure your API keys

Edit the .env file that was created and add your API keys:
- Get your ScrapeCreators API key at scrapecreators.com
- Get your Gemini API key at Google AI Studio (optional, for video analysis)
Follow the displayed MCP configuration

The install script will show you the exact configuration to add to Claude Desktop or Cursor.

Manual Install

If you prefer to install manually:

Clone this repository

git clone https://github.com/trypeggy/facebook-ads-library-mcp.git
cd facebook-ads-library-mcp

Create a virtual environment and install dependencies

python3 -m venv venv
./venv/bin/pip install -r requirements.txt

Configure API keys

Copy the template and configure your API keys:
```
cp .env.template .env
# Then edit .env with your actual API keys
```
To obtain API keys:
- Sign up for Scrape Creators here
- Get a Google Gemini API key here (optional, for video analysis)
Connect to the MCP server

Add the MCP server configuration to your Claude Desktop or Cursor config:
```
{
  "mcpServers": {
    "fb_ad_library": {
      "command": "{{PATH_TO_PROJECT}}/facebook-ads-library-mcp/venv/bin/python",
      "args": [
        "{{PATH_TO_PROJECT}}/facebook-ads-library-mcp/mcp_server.py"
      ]
    }
  }
}
```
Replace {{PATH_TO_PROJECT}} with the full path to where you cloned this repository.

Note: The configuration uses the virtual environment's Python interpreter (venv/bin/python) for better dependency isolation and reliability.

Note: API keys are now automatically loaded from the .env file, so you don't need to pass them as command line arguments.

For Claude Desktop:

Save this as claude_desktop_config.json in your Claude Desktop configuration directory at:
```
~/Library/Application Support/Claude/claude_desktop_config.json
```
For Cursor:

Save this as mcp.json in your Cursor configuration directory at:
```
~/.cursor/mcp.json
```
Restart Claude Desktop / Cursor

Open Claude Desktop and you should now see the Facebook Ads Library as an available integration.

Or restart Cursor.

Technical Details

Claude sends requests to the Python MCP server
The MCP server intelligently batches and optimizes queries to the ScrapeCreators API
Smart caching reduces redundant API calls and improves performance
Credit monitoring prevents workflow interruption with proactive error handling
Data flows back through the chain to Claude with enhanced batch information

Available MCP Tools (Enhanced)

This MCP server provides tools for interacting with Facebook Ads library objects:

Tool Name	Description	Batch Support
`get_meta_platform_id`	Returns platform ID given one or many brand names	✅ Multiple brands
`get_meta_ads`	Retrieves ads for specific page(s) (platform ID)	✅ Multiple platform IDs
`analyze_ad_image`	Analyzes ad images for visual elements, text, colors, and composition	⚡ Enhanced caching
`analyze_ad_video`	Analyzes single ad video using Gemini AI for comprehensive insights	⚡ Enhanced caching
`analyze_ad_videos_batch`	NEW - Analyzes multiple videos in 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	-

🆕 Enhanced Features:

Batch Processing: get_meta_platform_id and get_meta_ads now accept single strings or arrays for efficient bulk operations
Video Batch Analysis: analyze_ad_videos_batch processes multiple videos in a single Gemini API call for massive token savings
Smart Credit Management: All tools detect API credit exhaustion and provide direct links to top-up
Request Deduplication: Automatic removal of duplicate requests in batch operations
Token Optimization: Shared prompt processing reduces Gemini API costs by ~88% for video analysis
Backward Compatibility: All existing single-query workflows continue to work unchanged

Troubleshooting

Common Issues

🆕 ScrapeCreators API Credits Exhausted:

When you see "ScrapeCreators API credits exhausted", you need to top up your account
The error message includes a direct link to ScrapeCreators Dashboard)
You can check your current credit balance and purchase more credits there
The server will automatically resume working once credits are available

🆕 Rate Limit Exceeded:

If you hit rate limits, the server will tell you how long to wait
Batch operations help reduce the chance of hitting rate limits
Consider spacing out large batch requests if you frequently hit limits

API Key Not Found Error:

Ensure your .env file is in the project root directory
If you don't have a .env file, copy it from the template: cp .env.template .env
Check that your API keys are correctly formatted without quotes
Verify the .env file contains SCRAPECREATORS_API_KEY=your_key_here
For video analysis, ensure GEMINI_API_KEY=your_key_here is also added

Video Analysis Not Working:

Confirm you have a valid Google Gemini API key in your .env file
Video analysis requires the GEMINI_API_KEY environment variable

MCP Server Connection Issues:

Verify the path in your MCP configuration points to the correct location
Make sure you've created a virtual environment and installed dependencies with python3 -m venv venv && ./venv/bin/pip install -r requirements.txt
Ensure your MCP configuration uses the virtual environment Python path (ending with /venv/bin/python)
Restart Claude Desktop/Cursor after configuration changes

For additional Claude Desktop integration troubleshooting, see the MCP documentation. The documentation includes helpful tips for checking logs and resolving common issues.

Feedback

Your feedback will be massively appreciated. Please tell us which features on that list you like to see next or request entirely new ones.

License

This project is licensed under the MIT License.