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Lab 028: Deploy MCP Server to Azure Container Apps

Level: L300 Path: MCP Time: ~60 min 💰 Cost: ~$0 (scale-to-zero) + Azure Container Registry ~$5/month

Azure subscription required

This lab deploys Azure Container Apps. → Prerequisites guide

What You'll Learn

  • Containerize an MCP server (Python) with Docker
  • Push the image to Azure Container Registry
  • Deploy to Azure Container Apps with SSE transport
  • Update the MCP server with zero-downtime rolling deploys
  • Connect from GitHub Copilot and any MCP client

Prerequisites

Deploy Infrastructure

Option A — Deploy to Azure (one click)

Deploy to Azure

This deploys: - Azure Container Apps Environment + Log Analytics - A placeholder container app (you'll update it with your image in Step 3)

Option B — Azure CLI (Bicep)

git clone https://github.com/lcarli/AI-LearningHub.git && cd AI-LearningHub

az group create --name rg-ai-labs-mcp --location eastus

az deployment group create \
  --resource-group rg-ai-labs-mcp \
  --template-file infra/lab-028-mcp-container-apps/main.bicep

# Get the app URL
az deployment group show \
  --resource-group rg-ai-labs-mcp \
  --name main \
  --query properties.outputs.appUrl.value -o tsv

Lab Exercise

Step 1: Create the MCP server (SSE transport)

For remote deployment, use SSE (Server-Sent Events) transport instead of stdio.

mcp-product-server/
├── server.py
├── Dockerfile
└── requirements.txt

server.py:

import os, csv, json
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("OutdoorGear Product Server")

# Load product catalog from GitHub raw URL or local file
PRODUCTS_URL = "https://raw.githubusercontent.com/lcarli/AI-LearningHub/main/data/products.csv"

import urllib.request, io

def load_products() -> list[dict]:
    with urllib.request.urlopen(PRODUCTS_URL) as r:
        content = r.read().decode("utf-8")
    return list(csv.DictReader(io.StringIO(content)))

PRODUCTS = load_products()

@mcp.tool()
def search_products(query: str) -> str:
    """Search outdoor gear products by keyword."""
    q = query.lower()
    matches = [
        p for p in PRODUCTS
        if q in p["name"].lower() or q in p["category"].lower() or q in p["description"].lower()
    ]
    if not matches:
        return "No products found."
    return json.dumps(matches[:5], indent=2)

@mcp.tool()
def get_product(sku: str) -> str:
    """Get full details for a product by SKU."""
    for p in PRODUCTS:
        if p["sku"].lower() == sku.lower():
            return json.dumps(p, indent=2)
    return f"Product with SKU '{sku}' not found."

@mcp.tool()
def list_categories() -> str:
    """List all available product categories."""
    cats = sorted(set(f"{p['category']}/{p['subcategory']}" for p in PRODUCTS))
    return "\n".join(cats)

if __name__ == "__main__":
    # Use SSE transport for remote/cloud deployment
    mcp.run(transport="sse", host="0.0.0.0", port=int(os.environ.get("PORT", 8000)))

requirements.txt: 

mcp[cli]>=1.0.0

Dockerfile: 

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY server.py .
EXPOSE 8000
CMD ["python", "server.py"]

Step 2: Test locally

cd mcp-product-server
pip install mcp
python server.py
# Server running on http://0.0.0.0:8000

# Test in another terminal
npx @modelcontextprotocol/inspector http://localhost:8000/sse

Step 3: Build and push container image

# Variables — update these
RESOURCE_GROUP="rg-ai-labs-mcp"
ACR_NAME="mcpacr$(date +%s | tail -c 6)"   # unique name

# Create Azure Container Registry (Basic tier ~$5/month)
az acr create --resource-group $RESOURCE_GROUP --name $ACR_NAME --sku Basic

# Build and push
az acr build --registry $ACR_NAME --image mcp-product-server:latest .

# Get the image URL
IMAGE_URL="${ACR_NAME}.azurecr.io/mcp-product-server:latest"
echo "Image: $IMAGE_URL"

Step 4: Deploy to Container Apps

# Get the app name from deployment outputs
APP_NAME=$(az deployment group show \
  --resource-group $RESOURCE_GROUP \
  --name main \
  --query properties.outputs.appName.value -o tsv)

ENV_NAME=$(az deployment group show \
  --resource-group $RESOURCE_GROUP \
  --name main \
  --query properties.outputs.environmentName.value -o tsv)

# Enable Container Apps to pull from ACR
az containerapp registry set \
  --name $APP_NAME \
  --resource-group $RESOURCE_GROUP \
  --server "${ACR_NAME}.azurecr.io" \
  --identity system

# Update the container app with your image
az containerapp update \
  --name $APP_NAME \
  --resource-group $RESOURCE_GROUP \
  --image $IMAGE_URL

# Get the public URL
az containerapp show \
  --name $APP_NAME \
  --resource-group $RESOURCE_GROUP \
  --query properties.configuration.ingress.fqdn -o tsv

Step 5: Connect from GitHub Copilot

Add to .vscode/mcp.json:

{
  "servers": {
    "outdoorgear-cloud": {
      "type": "sse",
      "url": "https://YOUR-APP-NAME.YOUR-REGION.azurecontainerapps.io/sse"
    }
  }
}

Test in Copilot Agent Mode: "What camping tents do you have in stock?"

Step 6: Zero-downtime updates

# Update to a new version
az acr build --registry $ACR_NAME --image mcp-product-server:v2 .

az containerapp update \
  --name $APP_NAME \
  --resource-group $RESOURCE_GROUP \
  --image "${ACR_NAME}.azurecr.io/mcp-product-server:v2"

# Container Apps performs a rolling update automatically

Cost Breakdown

Resource SKU Estimated Cost
Container Apps Scale-to-zero (0 replicas when idle) ~$0 when idle
Container Apps (active) 0.5 vCPU / 1Gi ~$0.02/hour active
Azure Container Registry Basic ~$5/month
Log Analytics PerGB2018 ~$2/month (light usage)

Scale to zero

Container Apps scales to 0 replicas when there are no requests. For a dev/lab MCP server with occasional use, you'll pay almost nothing.

Cleanup

az group delete --name rg-ai-labs-mcp --yes --no-wait

Next Steps