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Lab 030: Microsoft Foundry Agent Service + MCP

Level: L300 Path: Foundry + MCP Time: ~90 min 💰 Cost: Azure subscription — gpt-4o-mini token costs

Azure subscription required

This lab requires an Azure subscription. → Prerequisites guide

What You'll Learn

  • Deploy Azure AI Foundry Hub + Project with one click
  • Deploy a gpt-4o-mini model endpoint
  • Create an Agent in the Foundry Agent Service
  • Connect your MCP server as a tool for the agent
  • Run an end-to-end agent conversation with tool calls

Deploy Infrastructure

Option A — Deploy to Azure (one click)

Deploy to Azure

This deploys: - Azure AI Foundry Hub - AI Project (OutdoorGear Agent Project) - Storage Account + Key Vault (required by Foundry)

Recommended location

Use East US or Sweden Central for best model availability.

Option B — Azure CLI (Bicep)

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

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

# Get your user Object ID
USER_OID=$(az ad signed-in-user show --query id -o tsv)

az deployment group create \
  --resource-group rg-ai-labs-foundry \
  --template-file infra/lab-030-foundry/main.bicep \
  --parameters location=eastus userObjectId=$USER_OID

Lab Exercise

Step 1: Deploy gpt-4o-mini in Foundry

  1. Go to ai.azure.com → your project
  2. Model catalog → search gpt-4o-mini → Deploy
  3. Name: gpt-4o-mini — keep defaults
  4. Copy your endpoint URL and API key from the deployment page

Step 2: Start your MCP server

Use the server from Lab 020 or Lab 028. For local testing:

python mcp_product_server.py  # starts on stdio or SSE

Step 3: Create an agent with Foundry SDK

pip install azure-ai-projects azure-identity

import os
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential

# Get these from your Foundry project settings
PROJECT_CONNECTION_STRING = os.environ["AZURE_AI_PROJECT_CONNECTION_STRING"]

client = AIProjectClient.from_connection_string(
    credential=DefaultAzureCredential(),
    conn_str=PROJECT_CONNECTION_STRING,
)

# Create the agent
agent = client.agents.create_agent(
    model="gpt-4o-mini",
    name="OutdoorGear Assistant",
    instructions=(
        "You are a helpful customer service agent for OutdoorGear Inc. "
        "Help customers find products, answer policy questions, and assist with orders. "
        "Be friendly, concise, and accurate. "
        "When you don't know something, say so honestly."
    ),
)
print(f"Created agent: {agent.id}")

Step 4: Run a conversation

# Create a thread (conversation session)
thread = client.agents.create_thread()

# Add a user message
client.agents.create_message(
    thread_id=thread.id,
    role="user",
    content="Hi! I'm looking for a tent for a winter camping trip to Mt. Rainier. What do you recommend?"
)

# Run the agent
run = client.agents.create_and_process_run(
    thread_id=thread.id,
    assistant_id=agent.id,
)

print(f"Run status: {run.status}")

# Get the response
messages = client.agents.list_messages(thread_id=thread.id)
for msg in messages.data:
    if msg.role == "assistant":
        for content in msg.content:
            if content.type == "text":
                print(f"\n🤖 {content.text.value}")

Step 5: Add MCP tools to the agent

# For Cloud MCP server (deployed via Lab 028):
agent_with_tools = client.agents.create_agent(
    model="gpt-4o-mini",
    name="OutdoorGear Assistant + Search",
    instructions="You are a helpful outdoor gear assistant. Use the search tool to find products.",
    tools=[
        {
            "type": "mcp",
            "server_label": "outdoorgear",
            "server_url": "https://YOUR-MCP-APP.azurecontainerapps.io/sse",
            "allowed_tools": ["search_products", "get_product", "list_categories"],
        }
    ]
)

# Now the agent can call your MCP server tools automatically
thread2 = client.agents.create_thread()
client.agents.create_message(
    thread_id=thread2.id,
    role="user",
    content="What climbing harnesses are currently in stock?"
)
run2 = client.agents.create_and_process_run(
    thread_id=thread2.id,
    assistant_id=agent_with_tools.id,
)

# Get response (agent will have called MCP tools)
messages2 = client.agents.list_messages(thread_id=thread2.id)
for msg in messages2.data:
    if msg.role == "assistant":
        print(msg.content[0].text.value)

Cleanup

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

Next Steps