About FbAdLibraryMCPServer
FbAdLibraryMCPServer accesses Facebook's Ad Library through web scraping for advertising research and analysis. Its search tool supports filters and sorting, and returns details such as advertiser, content, dates, impressions, and spend.
Key features
- 01Filter by advertiser, dates, and keywords
- 02Extract ad and delivery details
- 03Auto-scroll through search results
- 04Sort by date or relevance
- 05Return up to 100 ads per search
Use cases
- 01Research ads from an advertiser
- 02Find ads within a date range
- 03Analyze keywords in ad copy
- 04Organize results by date or relevance
MCP support
The MCP server exposes Facebook ad search through one `search_ads` tool, with parameters for advertiser, dates, keywords, result limit, and sorting.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEFacebook Ad Library MCP Server
A Model Context Protocol (MCP) server that provides access to Facebook's Ad Library through web scraping. This server exposes a single MCP tool for searching advertisements with comprehensive filtering options.
Features
- MCP-compliant: Built using the official MCP SDK
- Comprehensive ad data: Extracts advertiser, content, dates, impressions, spend, and more
- Advanced filtering: Filter by company, date range, and keywords
- Smart pagination: Auto-scrolling to retrieve up to 100 ads per search
- Flexible sorting: Sort by date (ascending/descending) or relevance
Installation
- Clone the repository:
git clone <repository-url>
cd FbAdLibraryMCPServer
- Install dependencies:
npm install
- Install Playwright browsers:
npx playwright install
Usage
Development
npm run dev
Production
npm run build
npm start
The server will start on port 3000 (configurable via PORT environment variable).
API Documentation
MCP Tool: search_ads
Search for Facebook ads with optional filters.
Parameters:
company(string, optional): Company/advertiser name to filter bystart_date(string, optional): Start date in YYYY-MM-DD formatend_date(string, optional): End date in YYYY-MM-DD formatkeywords(array of strings, optional): Keywords to search in ad contentlimit(integer, optional): Maximum ads to return (default: 50, max: 100)order(string, optional): Sort order - "date_desc", "date_asc", or "relevance" (default: "date_desc")
Example Tool Call:
{
"tool": "search_ads",
"arguments": {
"company": "Nike",
"start_date": "2024-01-01",
"end_date": "2024-01-31",
"keywords": ["running", "shoes"],
"limit": 10,
"order": "date_desc"
}
}
Response Format:
{
"ads": [
{
"id": "unique_ad_identifier",
"advertiser": "Company Name",
"content": "Ad text content",
"start_date": "Jan 15, 2024",
"end_date": "Feb 15, 2024",
"impressions": "1,000-5,000",
"spend": "$100-$500",
"platforms": ["Facebook"],
"creative_url": "https://...",
"demographics": {"targeting": "..."},
"scraped_at": "2024-01-20T10:30:00Z"
}
]
}
MCP Client Connection
Connect to the server using any MCP-compatible client:
HTTP Transport:
- URL:
http://localhost:3000/mcp - Method: POST
- Headers:
Content-Type: application/json
Health Check:
- URL:
http://localhost:3000/healthz - Method: GET
Architecture
- MCP Server (
src/index.ts): Express HTTP server hosting MCP protocol - Search Handler (
src/mcp/searchAdsHandler.ts): Core business logic - Web Scraper (
src/scraper/scrapeFacebookAds.ts): Playwright-based Facebook scraper - Filters (
src/utils/filters.ts): Date parsing and filtering utilities
Performance Considerations
- Rate Limiting: Built-in delays between requests to avoid blocking
- Pagination: Auto-scrolling with intelligent stopping conditions
- Browser Management: Proper cleanup of Playwright browser instances
- Memory Efficiency: Streaming data processing where possible
Anti-Bot Detection Countermeasures
This server successfully bypasses Facebook's anti-bot detection through:
- Virtual Display (Xvfb): Runs non-headless browsers in cloud environments
- Natural Navigation: Mimics human browsing patterns (homepage → Ad Library)
- Simplified Browser Config: Avoids complex "stealth" techniques that trigger detection
- Smart Timeout Handling: 30-second browser launch timeout with fallback mechanisms
- Production-Optimized: Different behavior in production vs development environments
Known Limitations
- Runs in non-headless mode to avoid detection
- Limited to 100 ads per search to prevent timeouts
- Date formats depend on Facebook's display format
- Subject to Facebook's anti-bot measures
Legal & Ethical Notes
- Respects Facebook's rate limiting through built-in delays
- Does not store personal user data
- Intended for research and analysis purposes
- Users should comply with Facebook's Terms of Service
Dependencies
@modelcontextprotocol/sdk: MCP protocol implementationplaywright: Web scraping automationdate-fns: Date parsing and manipulationexpress: HTTP server frameworkzod: Runtime type validation
Deployment (Fly.io)
- Install flyctl:
curl -L https://fly.io/install.sh | sh
export FLYCTL_INSTALL="/root/.fly"
export PATH="$FLYCTL_INSTALL/bin:$PATH"
- Deploy to Fly.io:
flyctl auth signup
flyctl launch --no-deploy
flyctl secrets set NODE_ENV=production
flyctl deploy
- Live Deployment:
- App URL:
https://fbadlibrarymcpserver.fly.dev - Health check:
https://fbadlibrarymcpserver.fly.dev/healthz - MCP endpoint:
https://fbadlibrarymcpserver.fly.dev/mcp(POST only) - Test endpoint:
https://fbadlibrarymcpserver.fly.dev/test-search(GET/POST)
- App URL:
Connection Details
Public URL: https://fbadlibrarymcpserver.fly.dev
Quick Test: https://fbadlibrarymcpserver.fly.dev/test-search?company=Nike&limit=3
MCP Client Configuration:
{
"transport": "http",
"url": "https://fbadlibrarymcpserver.fly.dev/mcp",
"method": "POST",
"headers": {
"Content-Type": "application/json",
"Accept": "application/json, text/event-stream"
}
}
Authentication: None required
License
ISC
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.