Free Udemy Course 2026: MCP for devs - Master Model Context Protocol - Python/TS

Unlock the Future of AI DevelopmentReady to take your AI applications to the next level? The Model Context Protocol (MCP) is revolutionizing how AI systems interact with external tools, data sources, and services. This comprehensive course will transform you from an MCP beginner into a confident builder of production-ready AI integrations.What You'll MasterFoundation & Setup Start strong with a solid understanding of MCP architecture, hosts, and the ecosystem. You'll set up your development environment and connect your first external MCP within minutes.Hands-On Building in Two Languages Choose your weapon—or learn both! Build complete MCP servers in TypeScript and Python with parallel implementations that let you compare approaches and pick what works best for your projects.Core Concepts That MatterNavigate MCP transport layers including HTTP implementationsMaster the three MCP primitives: Tools, Resources, and PromptsUnderstand when to use Resources vs Tools for optimal performanceDebug and inspect your MCPs like a proReal-World Practice Project Put theory into action with a complete, practical MCP project where you'll build a fully functional pizza ordering system. Learn to handle HTTP calls, display images in responses, manage parameters, and track orders through a real-world implementation.Why This Course?Parallel Language Support: Every major concept taught in both TypeScript and PythonProgressive Learning: From "Hello World" to complex integrationsProduction-Ready Skills: Learn patterns used in real applicationsHands-On Practice: Build actual working projects, not just toy examplesWho This Is ForDevelopers looking to integrate AI into existing applicationsAI enthusiasts ready to move beyond basic chatbot interactionsBackend engineers exploring modern AI architecturesAnyone curious about building the infrastructure that powers next-generation AI applicationsBy the End of This CourseYou'll confidently build custom MCP servers that extend AI capabilities with external tools, serve dynamic resources, and create seamless integrations. Whether you're building internal tools, commercial products, or experimental projects, you'll have the skills to make AI work with your systems, not just alongside them.The future of AI is extensible. Learn to build it.