Hands-On LabAdvanced

Building Interactive MCP Apps with Azure Functions

Build an MCP App that returns interactive HTML interfaces and deploy it to Azure Functions in this hands-on lab.

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60 minEstimated time
4Guided steps
AutoVerification
IsolatedSandbox
Building Interactive MCP Apps with Azure Functions

Lab overview

The Model Context Protocol (MCP) enables AI assistants to interact with external tools and data sources. Standard MCP tools return text responses, but MCP Apps take this further by delivering interactive HTML interfaces directly within AI chat conversations. This lab guides you through building and deploying an MCP App on Azure Functions that serves rich, interactive UIs instead of plain text.

You will scaffold an Azure Functions project with the MCP extension, create an MCP tool that declares UI resource availability via the ui:// URI scheme, build a self-contained HTML/TypeScript frontend using the @modelcontextprotocol/ext-apps SDK and Vite, and deploy the app to a Flex Consumption plan. By the end of the lab, you will test the interactive HTML rendering inside both Claude Desktop and VS Code with GitHub Copilot, experiencing the full MCP Apps workflow from development to deployment.

Objectives

Upon completing this Advanced level lab, you will be able to:

  • Understand the difference between standard MCP tools (text responses) and MCP Apps (interactive HTML interfaces)
  • Create an Azure Functions MCP tool with McpMetadata declaring UI resource availability via the ui:// URI scheme
  • Build a self-contained HTML/TypeScript frontend for the MCP App interface using Vite
  • Serve the HTML bundle via McpResourceTrigger with MIME type text/html;profile=mcp-app
  • Deploy the MCP App to Azure Functions on a Flex Consumption plan
  • Test the interactive UI rendering inside AI chat clients (Claude Desktop and VS Code with GitHub Copilot)

Who is this lab for?

This lab is designed for:

  • Cloud developers building AI-integrated applications who want to go beyond text-only MCP tools and deliver rich interactive experiences through MCP servers
  • Platform engineers exploring how to deploy and manage MCP Apps on Azure serverless infrastructure

Verified against your live environment

An automated validation engine inspects your actual resources and configurations as you work. Completion means the task was performed — not multiple choice, real-world proficiency.

[CHECK] validation_activelive
Inspecting deployed resources...
Verifying configuration state...
✓ Step requirements satisfied

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Related reading

Azure Compute Services Compared — VMs vs. App Service vs. Functions vs. Container AppsA practical comparison of Azure VMs, App Service, Functions, and Container Apps — what each does, what it costs, and when to use it.Azure AI Services Explained: A Beginner's Map to Vision, Speech, Language, and OpenAIA beginner's map to Azure AI Services. What Vision, Speech, Language, Content Safety, and Azure OpenAI each do, when to use them, and a week-by-week path with hands-on labs to start building today.Azure Serverless: Perfect for Cash-Conscious StartupsDiscover why Azure Serverless is the ideal solution for startups looking to optimize costs while scaling their infrastructure.
PremiumIncluded in Premium
Duration
60 min
Steps
4

Environment

Browser Code IDELive Cloud Environment

Every lab includes

  • Real environment, pre-credentialed
  • Automated checks on every step
  • Isolated sandbox, auto cleanup
  • AI-recommended next steps

Lab curriculum

  1. 01

    Logging into Azure CLI

  2. 02

    Scaffold the Project and Create an MCP Tool with UI Metadata

    1 automated check

  3. 03

    Build the Interactive HTML Frontend for the MCP App

    1 automated check

  4. 04

    Deploy the MCP App to Azure Functions and Test the Interactive UI

    1 automated check

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