Frequently Asked Questions about Crawl4AI RAG MCP Server

Q: What is an MCP Server? A: MCP stands for Model Context Protocol. An MCP server acts as a bridge, enabling AI models to access and interact with external data sources and tools.

Q: What are the prerequisites for using the Crawl4AI RAG MCP Server? A: You need Docker or Python 3.12+, a Supabase account, and an OpenAI API key. See the Installation section for details.

Q: How does the Crawl4AI RAG MCP Server integrate with UBOS? A: The Crawl4AI RAG MCP Server seamlessly integrates with the UBOS platform, allowing you to easily deploy and manage the server within your UBOS environment.

Q: What is RAG and why is it important? A: RAG stands for Retrieval-Augmented Generation. It allows AI models to generate more contextually relevant and informative responses by retrieving information from external knowledge sources.

Q: What types of URLs can the smart_crawl_url tool handle? A: It can handle regular webpages, sitemaps, and text files.

Q: What are the different RAG strategies and when should I use them? A: The server supports Contextual Embeddings, Hybrid Search, Agentic RAG, and Reranking. See the RAG Strategy Options section for details on when to use each strategy.

Q: What is Agentic RAG and when should I use it? A: Agentic RAG enables specialized code example extraction and storage, making it essential for AI coding assistants needing to find specific code implementations.

Q: How can I contribute to the development of the Crawl4AI RAG MCP Server? A: You can contribute by submitting pull requests, reporting issues, or suggesting new features on the GitHub repository.

Q: Can I use other Vector Database instead of Supabase? A: Currently Crawl4AI RAG MCP Server use Supabase as Vector Database. But in the future releases, there will be options for different Vector Databases.

Q: Can I use other Embedding models instead of OpenAI? A: Currently Crawl4AI RAG MCP Server use OpenAI models. But in the future releases, there will be options for different Embedding models, even local.