MCP Agentic AI Crash Course With Python

Overview

Explore the Model Context Protocol for standardized LLM interactions, building MCP clients and servers, and implementing Python SDK for resource management and protocol handling.

Syllabus

• Introduction to Agentic AI and MCP
Definition and significance of Agentic AI
Overview of the Model Context Protocol (MCP)
Importance and applications of standardized LLM interactions
• Understanding the Model Context Protocol
Structure and components of MCP
Protocol semantics and operations
Use cases and practical examples
• Building MCP Clients
Setting up the development environment
Implementation of MCP client architecture
Handling requests and managing responses
Error handling and debugging
• Developing MCP Servers
Server-side protocol handling
Serving multiple clients concurrently
Security and access control measures
Performance optimization
• Implementing Python SDK for MCP
Introduction to the Python SDK for MCP
SDK installation and configuration
Resource management using the SDK
Advanced protocol handling techniques
• Practical Python Programming for MCP
Writing clear and efficient Python code
Using Python libraries and frameworks
Best practices for robust MCP interaction
• Case Studies and Applications
Analyzing real-world MCP implementations
Lessons learned and best practices
Future trends in Agentic AI and MCP
• Course Project: MCP Implementation
Defining project goals and requirements
Designing and developing an MCP solution
Testing and deployment strategies
Project review and feedback
• Conclusion and Further Learning
Summary of key concepts covered
Additional resources for continued learning
Opportunities for advanced study and applications

Subjects

Computer Science