Building an End-to-End MCP Application

Welcome to Unit 2 of the MCP Course!

In this unit, we’ll build a complete MCP application from scratch, focusing on creating a server with Gradio and connecting it with multiple clients. This hands-on approach will give you practical experience with the entire MCP ecosystem.

What You’ll Learn

In this unit, you will:

• Create an MCP Server using Gradio’s built-in MCP support
• Build a sentiment analysis tool that can be used by AI models
• Connect to the server using different client implementations:
  • A HuggingFace.js-based client
  • A SmolAgents-based client for Python
• Deploy your MCP Server to Hugging Face Spaces
• Test and debug the complete system

By the end of this unit, you’ll have a working MCP application that demonstrates the power and flexibility of the protocol.

Prerequisites

Before proceeding with this unit, make sure you:

• Have completed Unit 1 or have a basic understanding of MCP concepts
• Are comfortable with both Python and JavaScript/TypeScript
• Have a basic understanding of APIs and client-server architecture
• Have a development environment with:
  • Python 3.10+
  • Node.js 18+
  • A Hugging Face account (for deployment)

Our End-to-End Project

We’ll build a sentiment analysis application that consists of three main parts: the server, the client, and the deployment.

Server Side

• Uses Gradio to create a web interface and MCP server via gr.Interface
• Implements a sentiment analysis tool using TextBlob
• Exposes the tool through both HTTP and MCP protocols

Client Side

• Implements a HuggingFace.js client
• Or, creates a smolagents Python client
• Demonstrates how to use the same server with different client implementations

Deployment

• Deploys the server to Hugging Face Spaces
• Configures the clients to work with the deployed server

Let’s Get Started!

Are you ready to build your first end-to-end MCP application? Let’s begin by setting up the development environment and creating our Gradio MCP server.

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