Research competitor ads and creative strategies on Facebook
Facebook Ads Library MCP server searches and analyzes any brand's public Facebook ads, including AI-powered video ad analysis.
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Why it matters
Conduct comprehensive competitive intelligence by analyzing Facebook Ads Library data to understand competitor messaging, creative approaches, and advertising strategies without manual searching.
Outcomes
What it gets done
Extract ad creative and messaging from Facebook Ads Library
Analyze competitor advertising patterns and trends
Compare multiple ad campaigns side-by-side
Test and evaluate creative approaches across advertisers
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/vb-facebook-ads-library | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
Facebook Ads Library MCP Server
An MCP server that exposes Meta's public Facebook Ads Library, letting an AI assistant search a brand's running ads and analyze images, text, and video with Gemini AI. It supports batch comparison across multiple competing brands. Use it when researching a brand's or competitors' running Facebook ad campaigns and their messaging or creative strategy.
What it does
Facebook Ads Library MCP Server exposes Meta's public Facebook Ads Library to an AI assistant through the Model Context Protocol. It lets you search for any company or brand's currently running ads, analyze ad images and text, run comprehensive video-ad analysis via Gemini AI, compare multiple companies' advertising strategies side by side, and surface insights into what messaging and creative approaches are working in their campaigns - useful for single-brand deep dives or batch comparisons across several competitors at once.
When to use - and when NOT to
Use it when you want an AI assistant to pull and analyze a brand's real running Facebook ads - single-brand questions like how many ads a brand is running and their video/image split, or batch comparisons like contrasting the advertising strategies, ad volumes, and messaging themes across several competing brands at once. A hosted version at useproxy.dev needs no API keys, Python, or server to run and works across ChatGPT, Claude, Cursor, and Manus; self-hosting is only worth it if you specifically want to modify or extend the server's code, since it requires Python 3.12+, a ScrapeCreators API key, and optionally a Google Gemini API key for video analysis. It does not access private or unpublished ad data - only what Meta already exposes through its public Ads Library.
Capabilities
The server provides get_meta_platform_id to resolve one or many brand names to their platform ID, get_meta_ads to retrieve ads for one or more platform IDs, analyze_ad_image for visual/text/color/composition analysis, analyze_ad_video for single-video Gemini-based analysis, and the newer analyze_ad_videos_batch, which analyzes multiple videos in one API call for roughly 88% token savings versus analyzing them individually. It also exposes get_cache_stats, search_cached_media (by brand, colors, people, or media type), and cleanup_media_cache for managing its media cache. Under the hood it intelligently batches and caches queries to the underlying ads-data API and monitors API credits to avoid interrupting a workflow mid-query.
How to install
Self-hosting starts with cloning the repository and running the install script, which creates a virtual environment, installs dependencies, and sets up configuration files:
git clone https://github.com/proxy-intell/facebook-ads-library-mcp.git
cd facebook-ads-library-mcp
# For macOS/Linux:
./install.sh
# For Windows:
install.bat
After installing, add your ads-data API key as SCRAPECREATORS_API_KEY (and optionally GEMINI_API_KEY for video analysis) to the generated .env file, then follow the install script's displayed MCP configuration to connect it to Claude Desktop or Cursor and restart the client. Most users should instead use the hosted version at useproxy.dev, which skips all of this setup entirely.
Who it's for
Marketers, competitive-intelligence analysts, and agencies who want to research and compare competitors' running Facebook ad campaigns without manually browsing the Ads Library, and developers who want to self-host and extend the server's ad-analysis tools - the project is MIT-licensed - rather than use the managed hosted version.
Source README
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.
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
Hosted Version (Recommended)
The easiest way to use the Facebook Ads Library MCP is the hosted version from Proxy (useproxy.dev). No API keys, no Gemini key, no Python, no server to run - just connect and start querying.
- ⚡ Zero setup - nothing to install, configure, or maintain
- 🔑 No API keys - skip the ScrapeCreators and Gemini keys entirely
- 🔌 Works everywhere - ChatGPT, Claude, Cursor, Manus, and anywhere else that supports MCP
- 🚀 Always up to date - new tools and fixes ship automatically
👉 Get started for free at useproxy.dev →
Prefer to run it yourself? The full self-host setup is documented below.
Hosted vs. Self-Host
| Hosted - Proxy (useproxy.dev) | Self-Host | |
|---|---|---|
| Setup time | None - connect and go | Python env + config |
| API keys required | None | ScrapeCreators + Gemini |
| Infrastructure | Fully managed | You run and maintain it |
| Updates | Automatic | Manual git pull |
| Works in ChatGPT, Claude, Cursor, Manus | ✅ | ✅ |
| Best for | Most users who just want the data | Developers who want to customize the code |
For most people, the hosted version is the fastest path. Choose self-host if you specifically want to modify or extend the server yourself.
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 API key for an ads data provider, set as
SCRAPECREATORS_API_KEY(see configuration below) - A Google Gemini API key for video analysis (optional, only needed for video ads)
Prefer not to deal with API keys? See the Hosted Version above to skip setup entirely.
Quick Install (Recommended)
- Clone and run the install script
git clone https://github.com/proxy-intell/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.envfile that was created and add your API keys:
- Set your ads data API key as
SCRAPECREATORS_API_KEY - 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/proxy-intell/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:
To obtain API keys:
- Set your ads data API key as
SCRAPECREATORS_API_KEYin the.envfile - 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:
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.envfile, so you don't need to pass them as command line arguments.
For Claude Desktop:
Save this asclaude_desktop_config.jsonin your Claude Desktop configuration directory at:
For Cursor:
Save this asmcp.jsonin your Cursor configuration directory at: - 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 ads data 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 | - |
Troubleshooting
Common Issues
🆕 API Credits Exhausted:
- When you see an "API credits exhausted" message, you need to top up your account
- The error message includes a direct link to your provider's 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
.envfile is in the project root directory - If you don't have a
.envfile, copy it from the template:cp .env.template .env - Check that your API keys are correctly formatted without quotes
- Verify the
.envfile containsSCRAPECREATORS_API_KEY=your_key_here - For video analysis, ensure
GEMINI_API_KEY=your_key_hereis also added
Video Analysis Not Working:
- Confirm you have a valid Google Gemini API key in your
.envfile - Video analysis requires the
GEMINI_API_KEYenvironment 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.
FAQ
What is the easiest way to use the Facebook Ads Library MCP?
The easiest way is the hosted version from Proxy (useproxy.dev). It requires no API keys, no installation, and no server - you connect it to ChatGPT, Claude, Cursor, or any MCP client and start querying immediately. You can start for free.
Do I need an API key to use this MCP?
Only if you self-host. The hosted version at useproxy.dev handles all data access for you, so no ScrapeCreators or Gemini keys are needed. Self-hosting requires a SCRAPECREATORS_API_KEY (and a Gemini key for video analysis).
Which MCP clients does it work with?
Both the hosted and self-hosted versions work with ChatGPT, Claude (Desktop and web), Cursor, Manus, and any other client that supports the Model Context Protocol.
Is there a free version?
Yes - the hosted version from Proxy offers a free tier so you can start analyzing ads without any setup.
Should I self-host or use the hosted version?
Use the hosted version if you just want fast, reliable access to Facebook Ads Library data with zero maintenance - this fits most users. Self-host only if you want to modify or extend the server code yourself.
FAQ
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