Building Your First MCP Server

Welcome to the exciting part – building your first MCP server! This is where concepts become code, and you'll see how simple it can be to create powerful AI integrations.

An MCP server is like a smart assistant that knows how to do specific tasks. Today, we'll build a feedback collection server for our Cozy Café that can:

• Collect customer feedback
• Store it safely
• Analyze sentiment
• Provide summaries

Think of an MCP server as a translator between AI agents and your business tools. It speaks a standard language that any AI can understand.

MCP Server Components

Every MCP server has three main parts:

1. Resources - Data the server can access
2. Tools - Actions the server can perform
3. Server Core - Handles communication

Before coding, let's plan what our feedback server will do:

User Story

As a café owner, I want to:

• Collect feedback without interrupting staff
• Understand if customers are happy
• See trends over time
• Respond to issues quickly

Let's start with a basic MCP server structure. Create a new file:

Now let's implement the tool that actually collects feedback:

Adding Sentiment Analysis

Let's add a simple sentiment analysis tool:

Let's create a test script to make sure our server works:

Run the test:

You should see output showing successful feedback collection, sentiment analysis, and summary generation!

Let's add some professional touches to make our server robust:

Adding a Configuration File

Create a configuration file for easy customization:

<Quiz questions={[ { question: "What are the three main components of an MCP server?", options: [ "Database, API, Frontend", "Resources, Tools, and Server Core", "Input, Process, Output", "Client, Server, Network" ], correctAnswer: 1, explanation: "MCP servers have three main components: Resources (data access), Tools (actions), and the Server Core (handles communication)." }, { question: "What does the @server.list_tools() decorator do?", options: [ "It lists files in a directory", "It defines what actions the MCP server can perform", "It installs new tools", "It deletes unused tools" ], correctAnswer: 1, explanation: "The @server.list_tools() decorator defines the available tools (actions) that AI agents can call on this MCP server." }, { question: "Why do we validate input in the collect_feedback function?", options: [ "To make the code longer", "To ensure data quality and prevent errors", "Because Python requires it", "To slow down the server" ], correctAnswer: 1, explanation: "Input validation ensures we receive quality data, prevents errors, and protects our system from invalid or malicious input." }, { question: "What format does our MCP server use to communicate?", options: [ "HTTP REST API", "GraphQL", "Standard Input/Output (stdio)", "WebSockets" ], correctAnswer: 2, explanation: "This MCP server uses stdio (standard input/output) for communication, which allows it to work with any system that can read and write text streams." } ]} />