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Keep shoppingComplete MCP Bootcamp: Build Next-Gen AI Agents with MCP
8,297 students
Complete MCP Bootcamp: Build Next-Gen AI Agents with MCP
Master MCP to connect, extend, and automate LLMs — build context-aware, multi-agent AI systems from scratch
Last updated 7/2026
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What you'll learn
- Understand Model Context Protocol (MCP) and its role in building context-aware AI systems.
- Connect LLMs with tools, APIs, and real-world data using MCP.
- Build and deploy Agentic AI and RAG applications powered by MCP.
- Integrate MCP with frameworks like LangChain, LangGraph, and CrewAI.
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Course content
15 sections • 79 lectures • 11h 55m total length
Requirements
- Basic understanding of Python programming and APIs.
- Familiarity with Large Language Models (LLMs) such as OpenAI or Hugging Face.
- Some knowledge of AI or Machine Learning concepts is helpful but not mandatory.
- A computer with internet access to install required tools and frameworks.
Description
The Model Context Protocol (MCP) is transforming how modern AI systems operate. It is the emerging standard that allows Large Language Models (LLMs) to interact intelligently with external tools, APIs, and data sources. By learning MCP, you will understand how context flows between AI models and their environments, enabling the creation of truly autonomous and context-aware systems.
This course, Complete Model Context Protocol (MCP) Bootcamp, provides an in-depth understanding of how MCP works and how to implement it effectively in real-world AI applications. You will explore MCP’s architecture, its role in the Agentic AI ecosystem, and how it integrates with frameworks like LangChain, LangGraph, and CrewAI. The course is fully practical, project-based, and designed for professionals who want to build advanced AI workflows.
Introduction to Model Context Protocol (MCP):
Understand what MCP is and why it was introduced.
Learn how MCP changes the way LLMs communicate and share information.
Explore the problems MCP solves in modern Generative AI development.
Core Concepts and Architecture:
Study the main components of MCP, including models, tools, and context layers.
Understand how context is represented, managed, and exchanged.
Learn the design principles that make MCP scalable and extensible.
Building AI Systems with MCP:
Implement MCP-driven workflows using Python.
Connect language models with real-world APIs and databases.
Create context-aware applications capable of retrieving and reasoning with live data.
Build retrieval-augmented systems that integrate knowledge retrieval and response generation.
Integration with Leading Frameworks:
Use MCP with LangChain to enhance RAG pipelines.
Integrate MCP with LangGraph for stateful and graph-based reasoning.
Combine MCP with CrewAI to create multi-agent architectures.
Understand how MCP works with open-source and cloud-based LLMs such as OpenAI, Anthropic, and Mistral.
Projects You Will Build:
Project 1: Build a context-aware AI assistant using MCP.
Project 2: Connect an LLM to real-world APIs through MCP.
Project 3: Create an Autonomous RAG system with LangChain and MCP.
Project 4: Develop a multi-agent workflow using CrewAI and MCP.
Project 5: Deploy an MCP-powered AI system using Docker and GitHub Actions.
Security, Deployment, and Optimization:
Learn best practices for securing MCP communications and configurations.
Set up environments with Docker and VS Code for reproducible workflows.
Automate deployments and testing with GitHub Actions.
Who Should Take This Course:
AI engineers looking to build context-aware and autonomous systems.
Data scientists and ML developers exploring Agentic AI architectures.
Software engineers who want to connect LLMs with APIs and external tools.
Researchers and students interested in the evolution of context engineering.
Key Learning Outcomes:
Gain a complete understanding of how MCP enables structured model-to-tool communication.
Learn how to design and deploy intelligent systems that use dynamic context.
Acquire practical experience through multiple end-to-end projects.
Master the integration of MCP with frameworks used in modern AI development.
Technologies and Tools Covered:
Model Context Protocol (MCP)
LangChain, LangGraph, CrewAI
Python, OpenAI, Mistral, Anthropic
Vector Databases (FAISS, Chroma, Pinecone)
Docker, GitHub Actions, VS Code
About the Instructor:
Krish Naik has over 13 years of experience in the data analytics and AI industry and more than 7 years of experience teaching Machine Learning, Deep Learning, NLP, and Generative AI. Known for his practical, hands-on teaching approach, he has trained millions of learners to master real-world AI and data science concepts.
By the end of this course, you will have the skills to design, implement, and deploy MCP-powered AI systems. You will understand how MCP redefines model communication, how it enhances RAG systems, and how it enables the creation of intelligent, connected, and scalable Agentic AI applications.
Enroll today and become one of the first professionals to master the Model Context Protocol — the foundation of the next generation of AI development.
Who this course is for:
- AI engineers and developers who want to build context-aware and Agentic AI systems.
- Data scientists and ML engineers looking to integrate MCP into real-world projects.
- Software developers interested in connecting LLMs with external APIs and tools.
- Students and researchers exploring the future of context engineering and AI protocols.