Modern applications demand smarter, faster, and more adaptive backend systems. In this course, you’ll learn how to design and build MCP (Model Context Protocol) servers powered by AI to create scalable, intelligent, and tool-integrated applications.
This course is designed for developers who want to go beyond traditional APIs and build systems that can seamlessly connect AI models with real-world tools, data sources, and services. You’ll explore how MCP servers act as the bridge between large language models and external functionality—enabling dynamic, context-aware interactions.
Through hands-on projects and real-world examples, you will:
- Understand the fundamentals of MCP architecture and design patterns
- Build MCP servers that expose tools, resources, and structured capabilities
- Integrate AI models to enable intelligent decision-making and automation
- Implement context management, tool calling, and response orchestration
- Connect MCP servers to databases, APIs, and external systems
- Deploy and scale MCP servers for production environments
By the end of the course, you’ll be able to build robust MCP-powered backends that enhance AI applications with real-world functionality—unlocking a new generation of intelligent systems.
Curriculum
- 6 Sections
- 6 Lessons
- 6 Weeks
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- Module 1: MCP Architecture & Core ConceptsUnderstand the foundations of Model Context Protocol and how it reshapes the way AI systems interact with the world. You’ll explore how MCP differs from traditional REST APIs, examine the client-server communication model, and dissect the core primitives: tools, resources, and prompts. By the end of this module you’ll have a clear mental model of when and why MCP is the right architectural choice, and you’ll be ready to design your first server with confidence.6
- 1.1Lesson 1.1: What Is MCP and Why Does It Exist?20 Minutes
- 1.2Lesson 1.2: The MCP Client-Server Model25 Minutes
- 1.3Lesson 1.3: Core Primitives — Tools, Resources & Prompts30 Minutes
- 1.4Lesson 1.4: MCP vs Function Calling vs Traditional APIs20 Minutes
- 1.5Lesson 1.5: Common MCP Design Patterns25 Minutes
- 1.6Hands-On Exercise / Lab: Map Your First MCP System30 Minutes
- Module 2: Building Your First MCP ServerMove from theory to practice by building a functional MCP server from scratch. You’ll set up your development environment, choose the right SDK (Python or TypeScript), and implement your first tool and resource endpoints. Step-by-step, you’ll learn how to define schemas, handle requests, return structured responses, and test your server locally. This hands-on module eliminates the “blank page” problem so you can start shipping real functionality quickly.0
- Module 3: AI Integration & Intelligent Tool CallingConnect your MCP server to large language models and unlock dynamic, context-aware behavior. You’ll learn how LLMs decide which tools to call, how to structure tool descriptions for optimal AI reasoning, and how to manage multi-turn conversations with preserved context. Practical exercises cover chaining multiple tools, handling ambiguous inputs gracefully, and building orchestration logic that lets the model drive complex workflows without hardcoded control flow.0
- Module 4: Connecting Databases, APIs & External ServicesReal-world MCP servers need to reach beyond their own process. This module teaches you to wire your server to relational databases, vector stores, REST APIs, and third-party SaaS platforms. You’ll implement connection pooling, handle authentication securely, manage rate limits, and build resilient error-handling strategies. By the end you’ll have a reusable integration layer that your AI models can query and mutate just like any other tool in their toolkit.0
- Module 5: Advanced Patterns — Security, Streaming & Multi-Agent SystemsElevate your MCP servers with production-grade capabilities. You’ll implement input validation and output sanitization to prevent prompt-injection attacks, add streaming responses for long-running tools, and explore sampling controls that give MCP servers agency over model interactions. The module also covers multi-agent architectures where MCP servers expose capabilities to orchestrator agents — opening the door to collaborative AI workflows that are both powerful and auditable.0
- Module 6: Deploying & Scaling MCP Servers in ProductionShip your MCP server with confidence. You’ll containerize it with Docker, deploy to cloud environments (AWS, GCP, or Azure), and configure transport options — stdio for local agents, HTTP/SSE for remote clients. The module covers observability with structured logging and tracing, horizontal scaling strategies, and CI/CD pipelines that validate tool schemas on every commit. You’ll finish the course with a production-ready deployment checklist and a capstone project ready for real users.0
Instructor
$69.00