Discover how MCP can make integrating AI applications with surrounding systems easier than ever before! Learn about how MCP works, how to define your own MCP tools, and build the bridge between client and server.

MCP: AI Apps as Easy as 1, 2, 3

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Host

Client

Server

Getting to Know MCP

Your First MCP Server

Building Your First MCP Server

Building a Currency Converter MCP Server

Adding Docstrings and Type Hints

MCP Client-Server Communications

True

False

Building Bridges (Between the Client and Server)

Dynamic Tool Discoverability

Calling Server Tools

The Building Blocks of MCP

Add resources and prompts to MCP servers to expose LLMs to read-only context and key instructions to alter its behavior. Get hands-on with LLMs and connect them to your MCP server so it can call tools and retrieve context.

Resources in MCP Servers

Defining MCP Server Resources

Listing Resources from the Client

Reading Resources from the Client

Prompts in MCP Servers

Defining an MCP Server Prompt

Listing Prompts from the Client

Retrieving a Prompt from the Client

MCP and LLMs: Tools

The Tool-Calling Workflow

LLM Tool Use in MCP Servers

MCP and LLMs: Prompts and Resources

Fetch Resource and Prompt from MCP

Building the Message and Calling the LLM

MCP-Enabled LLM Applications

Find out what it takes to get MCP servers into production by taking a deep-dive into databases and APIs in MCP servers, and the additional considerations that they bring. Finally, integrate third-party MCPs securely and reliably, so you don't have to recreate the wheel for your favorite integrations.

MCP Database Integrations

Creating Database Connections

Databases as Resources

Parameterized Database Lookup Tools

MCP API Integrations

Making API Tool Calls Robust

When APIs Require Authentication

Third-Party MCP Servers

Search for a Book by Title with the Open Library MCP

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Preparing MCP Servers for Production

Gone is the headache of integrating AI applications with disparate external systems! Now, every software application out there is talking the MCP language. Find out how MCP makes it easier than ever to connect AI applications to APIs, databases, and other external functionality using its protocol. Learn how to build your own MCP servers from the ground-up, including API and database connections, and integrate them with LLMs so it can make smart data lookups and trigger actions. Get started with the Model Context Protocol today!

<h2>Why Learn the Model Context Protocol?</h2>Large language models can't access real-time data or take actions on their own, and wiring each tool or API with custom code doesn't scale. The Model Context Protocol (MCP) solves this with a single, standardized way for AI applications to connect to external tools, data, and services—often called "the USB-C port for AI." In this course, you'll build MCP servers and clients from scratch in Python and wire them to an LLM.<br><br><h2>How Do I Build and Connect My First MCP Server?</h2>You'll start by learning the MCP architecture—host, client, and server—and the three primitives every server exposes: tools, resources, and prompts. Then you'll build a currency converter server using FastMCP, add docstrings and type hints so an LLM can discover your tools, and write an async Python client that lists and calls those tools over stdio transport.<br><br><h2>How Do I Give an LLM Real-Time Tools and Context?</h2>Tools alone aren't enough—models also need data and behavioral instructions. You'll add resources for read-only context and prompts to guide the model when inputs are vague, then wire all three primitives into an OpenAI LLM using the five-step tool-calling workflow so it can answer confidently or ask for clarification when it should.<br><br><h2>How Do I Take MCP Servers to Production?</h2>Real-world servers need more than happy-path code. You'll swap file-based resources for database-backed queries, add request timeouts, structured error handling, and secure API authentication that keeps keys server-side. Finally, you'll connect to a third-party MCP server and see that the same client code works with any server that speaks the protocol.

Introduction to APIs in Python

Writing Functions in Python

Integrate AI/LLM applications with APIs, databases, and filesystems easier than ever before with the Model Context Protocol (MCP).

Introduction to Model Context Protocol (MCP)

🗄️ The *client* is responsible for starting a session with the server and sending it requests

🏗️ All messages between *client* and *server* in MCP follow the **JSON-RPC 2.0** format

🏃 When using **stdio** transport, the *server* continues running independently after the *client* exits

🌐 The **stdio** transport method requires a network connection to function properly

Building Bridges (Between the Client and Server)

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