This module introduces the fundamental concepts of AI agents and the crucial role external tools play in their capabilities. You'll learn what defines an AI agent, why standalone AI models are limited, and how tool calling and orchestration enable AI to perform real-world actions. Students will gain the skill to identify AI agents, explain the necessity of tools, and articulate the symbiotic relationship between agents, MCPs, and tools.

Dive into the core of Model Context Protocols (MCPs), the structured language that guides AI-tool interaction. This module defines MCPs, explains the problems they solve (like ambiguity), and breaks down their essential components, including different types of capabilities (Tools, Resources, Prompts, Sampling). Students will learn to define MCPs, identify their core elements, understand their problem-solving power, and distinguish them from simpler prompting methods.

This module focuses on the practical application of simple MCPs. You'll learn to interpret an MCP's structure, identify necessary inputs, and understand how it guides an AI to formulate tool requests and interpret responses. Through hands-on simulation in Claude Desktop, students will practice applying MCPs to real-world scenarios, gaining skills in interpreting MCPs, structuring AI interactions, and simulating tool-driven responses.

Become an MCP architect in this module! You'll learn the systematic principles for designing clear and effective MCPs from scratch, focusing on defining atomic goals and identifying all necessary contextual information. The module also introduces how AI-powered coding assistants like Cursor can significantly aid in MCP development. Students will acquire skills in formulating precise goals, designing MCP parameters, structuring interaction flows, and leveraging AI tools for MCP creation.

This concluding module consolidates your MCP knowledge, placing it within the broader landscape of Agentic AI. You'll recap the benefits and limitations of simple MCPs and explore where they fit into more complex AI systems like function calling and toolkits. Students will gain the ability to appreciate MCPs' role in advanced AI, summarize their pros and cons, recall best practices, and identify resources for continuous learning in AI tooling.