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Learn to build an AI-powered search engine using MCP (Model Context Protocol) servers in this comprehensive tutorial. Discover how to create a distributed system architecture with three integrated components: a Streamlit client for the AI chat interface, a Google Search MCP server for web search and content extraction, and a Perplexity MCP server for AI-powered search analysis. Explore the implementation of a four-layer platform architecture including user authentication, AI orchestration with LangChain agents, MCP protocol communication, and external data integration. Master the deployment of Docker containers to emulate a real distributed environment, implement multi-provider AI support with Azure OpenAI, and integrate smart caching mechanisms for both Google Custom Search API and Perplexity API responses. Gain hands-on experience with server-sent event communication, tool selection algorithms, prompt engineering techniques, and security implementations across the entire system stack.
