What is the Facebook Ads Library MCP Server?

The Facebook Ads Library MCP Server is a tool that allows you to access and analyze data from the Facebook Ads Library using the Model Context Protocol (MCP). It enables you to gain insights into competitor ad strategies, identify trends, and optimize your own campaigns.

How does the MCP Server work?

The MCP Server acts as a bridge between AI models (like those on the UBOS platform) and the Facebook Ads Library. It receives requests, queries the ScrapeCreator API for ad data, and returns the results to the AI model for analysis.

What is MCP (Model Context Protocol)?

MCP is an open protocol that standardizes how applications provide context to LLMs.

What can I do with the Facebook Ads Library MCP Server?

You can use it for competitive analysis, ad creative inspiration, trend identification, brand monitoring, campaign optimization, and market research.

What are the key features of the MCP Server?

Key features include seamless integration with UBOS, direct access to the Facebook Ads Library, advanced search and filtering, automated data extraction, and customizable reports.

How do I install the Facebook Ads Library MCP Server?

You can install it via Smithery or manually by cloning the repository, obtaining an API token from Scrape Creators, and configuring the necessary JSON files.

What are the prerequisites for installation?

Prerequisites include Python 3.12+, Pip, and an active Scrape Creators account.

Where can I find troubleshooting information?

Refer to the MCP documentation for troubleshooting tips and guidance.

How does this integrate with the UBOS platform?

UBOS allows you to orchestrate AI Agents, connect them with your enterprise data, build custom AI Agents with your LLM model and Multi-Agent Systems, enhancing the capabilities of the MCP server.

What are some example prompts I can use?

Example prompts include: “How many ads is ‘AnthropicAI’ running? What’s their split across video and image?”, “What messaging is ‘AnthropicAI’ running right now in their ads?”, and “Do a deep comparison to the messaging between ‘AnthropicAI’, ‘Perplexity AI’ and ‘OpenAI’. Give it a nice forwardable summary.”