
Discover the MCP model context protocol, learn how it works, and build, test locally, and deploy MCP servers so clients can use them; prerequisites include Python basics and AI fundamentals.
Standardize communication between AI models and data stores, CRMs, and more with MCP, the universal adapter for AI applications, reducing complexity from m×n to m+n with reusable MCP servers.
Explore how anyone can create an MCP server to wrap access to department services, detailing tool schemas, functions, templates, and the distinction from direct API calls.
Explore the MCP transports, a delivery system for MCP protocol messages from client to server, through JSON-RPC formats over stdio, SSE, or HTTP, with local vs remote trade-offs.
Run your first community MCP server on Claude Desktop and explore the Model Context Protocol’s open integration for LM applications with actor-critic thinking and dual-perspective analysis.
Explore how to build MCP resources for a library management system, returning a full catalog as JSON via a URI, using a mock database and resource annotations.
Add resources to the MCP server by switching from tool to resource, exposing resources for cuisines, recipes, meals, and stats, and generating markdown files with ingredients, summaries, links, and images.
Assemble MCP prompts with predefined templates for recipe search, meal planning, cooking lessons, ingredient exploration, and cultural cuisine, interpolating cuisine and recipe counts to produce complete prompts.
Test and debug the recipe MCP server on cloud desktop by installing with the MCP CLI, configuring cloud settings, running random name and nickname, and observing security prompts and docstrings.
Unlock the power of seamless AI integrations with the Model Context Protocol (MCP) in this comprehensive, hands-on course designed for developers and AI enthusiasts!
You'll dive deep into MCP, the universal adapter that's revolutionizing how AI applications communicate with external systems. Whether you're building LLM-powered tools or AI agents, MCP provides the standardized bridge between your AI and the world of APIs, databases, and services.
You'll start by exploring what MCP is and why it's essential for modern AI applications. Then we'll examine server-client architecture, dive into the three core MCP transports (STDIO, SSE, and Streamable HTTP), and understand how MCP solves critical integration challenges for LLMs and AI agents.
What You'll Learn:
MCP Fundamentals: Master the architecture, core concepts, and communication lifecycle of MCP servers and clients
Transport Protocols: Deep dive into STDIO, SSE, and Streamable HTTP transports with their pros and cons
Hands-on Development: Build multiple MCP servers from simple chat tools to complex SQL-powered systems
MCP Resources & Prompts: Implement advanced features like dynamic resources and intelligent prompting
Real-World Projects: Create a complete Recipe MCP Server with tools, resources, and prompts
Production Deployment: Deploy your MCP servers remotely and integrate them with VS Code, Claude Desktop, and other AI platforms
Testing & Debugging: Use MCP Inspector and other tools to thoroughly test your implementations
Who This Course is For:
Developers building AI agents and LLM applications
Software engineers wanting to integrate AI with existing systems
AI enthusiasts interested in cutting-edge integration protocols
Beginners with basic programming knowledge who want to build production-ready AI integrations
Hands-on Projects Include:
Community chat MCP server
SQLite-powered country data server
Complete recipe management system with search, storage, and meal planning
Remote deployment on Render with live testing
By the end of this course, you'll be able to build, test, and deploy MCP servers that seamlessly connect AI agents to any external system or API. You'll understand how to create the universal adapters that make AI applications truly powerful and connected.
Whether you're building chatbots, AI assistants, or complex agent systems, this course will equip you with the skills to create robust, scalable AI integrations using the industry's emerging standard.
This course is eligible for the Codestars Certificate Authority (CCA) certificate. Students can take the official exam via codestarscom, and those who pass the quiz will receive their CCA certificate. (more details in the course!)
Join now and master the protocol that's shaping the future of AI integrations!