by proxy-intell · MCP Server · ★ 236
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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.
| Stars | 236 |
| Forks | 33 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.05/100 |
| Last Updated | 2026-06-04 |
| Created | 2025-06-05 |
| Platforms | mcp, python |
| Est. Tokens | ~12k |
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facebook-ads-library-mcp is MCP Server for Facebook ADs Library - Get instant answers from FB's ad library. It is categorized as a MCP Server with 236 GitHub stars.
facebook-ads-library-mcp is primarily written in Python. It covers topics such as ai, analytics, api.
You can find installation instructions and usage details in the facebook-ads-library-mcp GitHub repository at github.com/proxy-intell/facebook-ads-library-mcp. The project has 236 stars and 33 forks, indicating an active community.
facebook-ads-library-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to facebook-ads-library-mcp on Agent Skills Hub include facebook-ads-library-mcp, instagram_dm_mcp, penpot-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.