VOOZH about

URL: https://anthropic.skilljar.com/introduction-to-model-context-protocol

⇱ Introduction to Model Context Protocol


Anthropic Academy Courses Sign In

About this course

This course provides comprehensive coverage of the Model Context Protocol (MCP), focusing on building both MCP servers and clients using the Python SDK. You'll learn about MCP's three core primitives—tools, resources, and prompts—and understand how they integrate with Claude AI to create powerful applications without writing extensive integration code.

What you'll learn

  • Understand MCP architecture and how it shifts tool definition and execution burden from your server to specialized MCP servers
  • Learn about MCP's transport-agnostic communication system and the message types used between clients and servers
  • Explore the complete request-response flow from user queries through MCP clients to external services and back to Claude
  • Build MCP servers using the Python SDK with decorators to define tools instead of writing JSON schemas manually
  • Implement document management functionality with tools for reading and editing documents using Field descriptions and type hints
  • Use the built-in MCP Server Inspector to test and debug your server functionality in a browser-based interface
  • Define resources for exposing read-only data, including both direct resources with static URIs and templated resources with parameters
  • Implement resource reading functionality in clients with proper MIME type handling for JSON and text content
  • Build prompts that provide pre-crafted, high-quality instructions for common workflows like document formatting
  • Understand when to use each MCP primitive: tools (model-controlled), resources (app-controlled), and prompts (user-controlled)
  • Examine practical integration patterns including autocomplete functionality and context injection for AI conversations

Prerequisites

  • Working knowledge of Python programming
  • Basic understanding of JSON and HTTP request-response patterns

Who this course is for

  • Developers looking to create MCP servers 

Curriculum

  • Introduction
  • Welcome to the course
  • Introducing MCP
  • MCP clients
  • Hands-on with MCP servers
  • Project setup
  • Defining tools with MCP
  • The server inspector
  • Course satisfaction survey
  • Connecting with MCP clients
  • Implementing a client
  • Defining resources
  • Accessing resources
  • Defining prompts
  • Prompts in the client
  • Assessment and wrap Up
  • Final assessment on MCP
  • MCP review

This course provides comprehensive coverage of the Model Context Protocol (MCP), focusing on building both MCP servers and clients using the Python SDK. You'll learn about MCP's three core primitives—tools, resources, and prompts—and understand how they integrate with Claude AI to create powerful applications without writing extensive integration code.

What you'll learn

  • Understand MCP architecture and how it shifts tool definition and execution burden from your server to specialized MCP servers
  • Learn about MCP's transport-agnostic communication system and the message types used between clients and servers
  • Explore the complete request-response flow from user queries through MCP clients to external services and back to Claude
  • Build MCP servers using the Python SDK with decorators to define tools instead of writing JSON schemas manually
  • Implement document management functionality with tools for reading and editing documents using Field descriptions and type hints
  • Use the built-in MCP Server Inspector to test and debug your server functionality in a browser-based interface
  • Define resources for exposing read-only data, including both direct resources with static URIs and templated resources with parameters
  • Implement resource reading functionality in clients with proper MIME type handling for JSON and text content
  • Build prompts that provide pre-crafted, high-quality instructions for common workflows like document formatting
  • Understand when to use each MCP primitive: tools (model-controlled), resources (app-controlled), and prompts (user-controlled)
  • Examine practical integration patterns including autocomplete functionality and context injection for AI conversations

Prerequisites

  • Working knowledge of Python programming
  • Basic understanding of JSON and HTTP request-response patterns

Who this course is for

  • Developers looking to create MCP servers 
  • Introduction
  • Welcome to the course
  • Introducing MCP
  • MCP clients
  • Hands-on with MCP servers
  • Project setup
  • Defining tools with MCP
  • The server inspector
  • Course satisfaction survey
  • Connecting with MCP clients
  • Implementing a client
  • Defining resources
  • Accessing resources
  • Defining prompts
  • Prompts in the client
  • Assessment and wrap Up
  • Final assessment on MCP
  • MCP review

Data and Privacy

Skilljar is a learning management system that hosts our educational content. You're logging into it to access the Anthropic course materials. This separate platform allows us to provide interactive learning experiences, track your progress, and ensure you have access to all course resources in an organized way.

Skilljar collects basic learning analytics such as course progress, lesson completion status, quiz scores, and time spent on materials. This data helps us understand how you're progressing through the course and allows us to provide you with completion certificates. All data collection is focused on improving your learning experience, and is subject to Skilljar's Privacy Policy.

Skilljar only tracks your learning progress within this course platform, while your Anthropic account manages your access to the Anthropic Console and/or Claude AI services.

Yes, Skilljar employs industry-standard security measures including data encryption, secure hosting, and regular security audits. Your learning data is stored on secure servers with appropriate access controls. Skilljar is SOC 2 compliant and follows best practices for data protection to ensure your information remains safe and private.

To request deletion of your learning data or account, email academy-support@anthropic.com. Your request will be processed in accordance with applicable privacy laws and our data retention policies. Note that some data may need to be retained for legitimate business purposes, such as compliance or security, but we'll delete all personal information where legally permissible.

No, you don't need an Anthropic account to access this learning content. The course is hosted on Skilljar and only requires a Skilljar account for access. However, if you want to use Claude AI services after completing the course, you would need to create a separate Anthropic account at claude.ai.

© 2025 Anthropic PBC