Get hands-on designing secure, intelligent AI agent workflows using the Model Context Protocol (MCP) in this labs-driven course. You’ll see how AI systems connect to external tools, services, and data sources. You’ll learn how those connections can be designed to stay safe and predictable using structured permissions, user prompts, and validation workflows. And in hands-on labs, you’ll build agents that reason, retrieve information, and carry out tasks while maintaining security and control.
Build AI Agents using MCP
Build AI Agents using MCP
This course is part of IBM RAG and Agentic AI Professional Certificate
Instructors: Abdul Fatir
Included with
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Explain the architecture, components, and use cases of the Model Context Protocol (MCP), and how it differs from traditional APIs and tool calling
Build and run MCP servers using FastMCP, configuring tools, resources, and prompts to support AI applications such as retrieval-augmented generation
Develop MCP clients that connect to single and multiple servers using STDIO and Streamable HTTP for structured, context-aware LLM interactions
Implement secure, interactive MCP workflows by applying sampling, roots, and permission-based user-approval mechanisms for multi-agent applications
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
February 2026
Assessments
10 assignments
Taught in English
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This course is part of the IBM RAG and Agentic AI Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from IBM
There are 3 modules in this course
In this module, you will gain a hands-on introduction to the Model Context Protocol (MCP). You will explore what MCP is, why it is used, and how it solves challenges compared to traditional APIs and tool-calling approaches. You will examine MCP's architecture, including clients, servers, and transport mechanisms, and see how MCP applications work in practice. Through guided demos and labs, you will connect to existing MCP servers and build your own MCP application.
What's included
9 videos1 reading3 assignments2 app items4 plugins
In this module, you will learn how to build and enhance MCP servers. You will begin by converting tools into MCP servers and exploring simple "Hello World" examples. You will then extend server functionality with resources, prompts, and tools for real-world applications such as retrieval-augmented generation (RAG). Finally, you will explore MCP transport mechanisms, including streamable HTTP, standard IO, and deprecated SSE, while considering their security and performance trade-offs. Through guided labs, you will build and run MCP servers, connect to them using different transports, and experiment with enhanced capabilities.
What's included
2 videos3 assignments2 app items2 plugins
In this module, you will learn how MCP clients are built and optimized for real-world use. You will examine client architecture, lifecycle management, and performance strategies such as connection pooling, caching, and load balancing. You will also explore advanced features like sampling and root controls to understand bidirectional LLM calls and filesystem boundaries. Finally, through guided labs, you will create custom MCP clients, implement advanced features, and design secure, interactive applications.
What's included
4 videos2 readings4 assignments3 app items2 plugins
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Frequently asked questions
This course equips professionals with valuable, hands-on skills used in roles such as MCP Developer, AI Agent Engineer, AI Tool Integration Specialist, Multi-Agent System Developer, and AI Workflow Engineer. It is ideal for software developers, Python programmers, and AI practitioners looking to expand into building and managing MCP-based AI applications. This course is also suitable for professionals reskilling to work on secure, multi-server AI agent systems.
You’ll need familiarity with basic programming skills (Python recommended) and a general understanding of how AI applications interact with tools and data sources. Completing the earlier courses in the IBM RAG and Agentic AI Professional Certificate is highly recommended for smooth progression.
You’ll work with MCP servers and clients, explore FastMCP, STDIO and HTTP transports, ReAct agents, and implement tools, prompts, resources, and user-aware workflows. Labs provide hands-on experience with multi-server interactions, context management, and secure elicitation workflows.
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Financial aid available,