This course delivers what many others don't - genuine hands-on experience developing and integrating and deploying MCPs for AI applications. You'll walk away with both the conceptual understanding and practical tools to tackle real-world Model Context Protocol challenges.

Latest Update 22 May:

• MCPs in OpenAI's Agents and Responses API 

• Secure Production MCPs using OAuth

MCPs in OpenAI's Agents and Responses API 

Secure Production MCPs using OAuth

Feedback from Students:

• * The course structure works brilliantly, starting with essential foundations and methodically building toward practical applications. * - Daniel

• * Zoltan does a great job at breaking down the concepts so it's easy to learn and build up your knowledge as you go." * -  Jose

• *Although I had some exposure to AI agents before, I still learned a lot that I can use in my daily work. *- Stefan

* The course structure works brilliantly, starting with essential foundations and methodically building toward practical applications. * - Daniel

* Zoltan does a great job at breaking down the concepts so it's easy to learn and build up your knowledge as you go." * -  Jose

*Although I had some exposure to AI agents before, I still learned a lot that I can use in my daily work. *- Stefan

Why Model Context Protocol matters: MCPs solve a critical problem in the multi-LLM world by creating standardized ways for AI models to interact with external systems. This bootcamp takes you from fundamental concepts and third-party MCP integration to an implementation of a real-world project at every step.

Course Structure: This course follows a hands-on, practical approach. We start with the theoretical foundations to understand MCPs in context, then quickly move to building real working applications.

THEORETICAL SECTION:

• How LLM interactions and tool calling work

• The problem MCPs solve in a multi-LLM world

• Core MCP concepts and architecture

• MCP features: Tools, Prompts, and more

• Where MCPs fit in the AI ecosystem

How LLM interactions and tool calling work

The problem MCPs solve in a multi-LLM world

Core MCP concepts and architecture

MCP features: Tools, Prompts, and more

Where MCPs fit in the AI ecosystem

PRACTICAL SECTION:

• Complete development environment setup for Mac & Windows

• Working with MCP hubs and global providers

• Integrating MCPs with Claude and Cursor

• Step-by-step creation of your own Crypto Price MCP

• Working with MCP Tools and Resources

• Testing and debugging MCPs with MCP Inspector

• Integrating MCPs into LangChain, LangGraph and LangSmith

• Building Python and Javascript-based MCPs

• Deploying MCPs to Production

• Securing MCPs with OAuth

• Deploying Secure MCPs to OAuth Workers

Complete development environment setup for Mac & Windows

Working with MCP hubs and global providers

Integrating MCPs with Claude and Cursor

Step-by-step creation of your own Crypto Price MCP

Working with MCP Tools and Resources

Testing and debugging MCPs with MCP Inspector

Integrating MCPs into LangChain, LangGraph and LangSmith

Building Python and Javascript-based MCPs

Deploying MCPs to Production

Securing MCPs with OAuth

Deploying Secure MCPs to OAuth Workers

OUTLOOK

• A sneek peak into Anthropic's MCP Roadmap about the future of the project

  
  

A sneek peak into Anthropic's MCP Roadmap about the future of the project

Join developers and AI enthusiasts already mastering MCP and transform your AI development skills today!