Adding Foundry Agent MCP Private Connectivity Lab

labs/ai-foundry-private-mcp/README.MD

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+# Azure AI Foundry Private Connectivity Lab
+
+A hands-on lab demonstrating secure agent consumption through private networking with Azure AI Foundry, API Management, and Front Door. Features MCP (Model Context Protocol) integration, private endpoints, and agent-based interactions. MCP servers are only accessible through the frontdoor. 
+
+
+## 🏗️ Architecture
+
+This lab demonstrates a fully private AI infrastructure:
+
+![architecture](architecture.png)
+
+Core Components:
+- **Azure AI Foundry**: AI Services account with private endpoint access only
+- **Azure API Management (APIM)**: Deployed in VNet, manages traffic to AI services via private endpoints
+- **Azure Front Door**: Premium tier with Private Link to APIM - the only publicly accessible endpoint
+- **Azure Key Vault**: Stores secrets, accessible only from private network
+- **Jumpbox VM**: Ubuntu VM with managed identity for testing from private network
+- **MCP Integration**: Model Context Protocol server exposed through APIM
+
+**Network Flow**: Client → Front Door (Public) → Private Link → APIM (Private VNet) → Private Endpoint → AI Foundry
+
+## 📋 Prerequisites
+
+- [Python 3.11 or later](https://www.python.org/) installed locally
+- [VS Code](https://code.visualstudio.com/) with [Jupyter extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) enabled
+- [Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli) installed and authenticated
+- [An Azure Subscription](https://azure.microsoft.com/free/) with Contributor permissions
+
+## 🚀 Deployment
+
+### 1. Clone and Setup
+
+```bash
+git clone <repository-url>
+cd AI-Gateway
+pip install -r requirements.txt
+```
+
+### 2. Deploy Infrastructure
+
+Open the [Jupyter notebook](./foundry-private-mcp.ipynb) and execute the cells to:
+
+1. **Initialize variables** - Set deployment name, locations, and model configurations
+2. **Create resource group** - Provision the Azure resource group
+3. **Deploy Bicep template** - Deploy all infrastructure (15-20 minutes):
+   - Virtual Network with subnets
+   - Azure AI Foundry with private endpoint
+   - APIM integrated in VNet
+   - Front Door with Private Link
+   - Key Vault with secrets
+   - Jumpbox VM with managed identity
+   - MCP API configuration
+
+4. **Approve Private Link** - Approve Front Door connection to APIM
+5. **Disable APIM public access** - Lock down APIM to private network only
+
+### 3. Verify Deployment Outputs
+
+The deployment provides:
+- `frontDoorEndpointHostName` - Public endpoint for testing
+- `apimResourceGatewayURL` - Private APIM URL (inaccessible from internet)
+- `keyVaultUrl` - Key Vault URL for secrets
+- `aiFoundryProjectEndpoint` - AI Foundry project endpoint
+
+## 🧪 Testing from Jumpbox
+
+### Connect to Jumpbox
+
+Use Azure Bastion to connect to the VM:
+
+```bash
+az network bastion ssh \
+  --name bastion-host \
+  --resource-group <resource-group> \
+  --target-resource-id <vm-resource-id> \
+  --auth-type password \
+  --username azureuser
+```
+
+Or connect via the Azure Portal: Navigate to the VM → Connect → Bastion
+
+### Create Python Scripts
+
+Once connected to the jumpbox, create the required scripts:
+
+#### 1. Create `scripts/load_env_from_kv.py`
+```bash 
+cat > ~/scripts/load_env_from_kv.py << 'EOF'
+# Paste content of agent/load_env_from_kv.py here
+EOF
+```
+
+#### 2. Create `scripts/sample_agents_mcp.py`
+```bash 
+cat > ~/scripts/sample_agents_mcp.py << 'EOF'
+# Paste content of agent/sample_agents_mcp.py here
+EOF
+```
+
+### Run the Agent
+
+```bash
+# Activate virtual environment
+source ~/venv/bin/activate
+
+# Get Key Vault URL from deployment outputs
+KEY_VAULT_URL="https://kv-xxxxx.vault.azure.net/"
+
+# Run the MCP agent
+python3 ~/scripts/sample_agents_mcp.py $KEY_VAULT_URL
+```
+
+The script will:
+1. Load secrets from Key Vault using managed identity
+2. Create an AI agent with MCP tools
+3. Send a test message ("Order sku-123 with 5 items")
+4. Process MCP tool calls through APIM
+5. Display the conversation and results
+
+### Test the Agent
+
+Now you can test the agent: 
+
+#### Test 1: Public Access to APIM
+
+- Access the APIM endpoint directly through its public URL
+- Expected Result: Request should be blocked or restricted, demonstrating that direct APIM access is protected
+
+![Execution Evidence](./docs/unauthorized_apim.png)
+
+##### Test 2: Unauthorized MCP Access
+
+- Attempt to call the MCP server without an authorization token
+- Expected Result: 401 Unauthorized, validating OAuth enforcement
+
+ ![Execution Evidence](./docs/unauthorized_mcp.png)
+
+##### Test 3: Authorized Access via Front Door
+
+- Access the MCP server through Front Door with a valid authorization token
+- Expected Result: Successful response, demonstrating the complete secure flow: Client → Front Door → Private Link → APIM → MCP Server
+
+![Execution Evidence](./docs/execution_evidence.png)
+
+## 🧹 Clean Up Resources
+
+When finished, delete all resources to avoid charges: <br>
+Use [clean-up-resources notebook](./clean-up-resources.ipynb).
+
+## 📚 Additional Resources
+
+- [Azure API Management Private Endpoints](https://learn.microsoft.com/azure/api-management/private-endpoint)
+- [Azure Front Door Private Link](https://learn.microsoft.com/azure/frontdoor/private-link)
+- [Azure AI Foundry Documentation](https://learn.microsoft.com/azure/ai-services/)
+- [Model Context Protocol](https://modelcontextprotocol.io/)

labs/ai-foundry-private-mcp/agent/load_env_from_kv.py

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+import os
+from azure.identity import DefaultAzureCredential
+from azure.keyvault.secrets import SecretClient
+
+def load_secrets_from_keyvault(vault_url: str):
+    """Load secrets from Azure Key Vault and set as environment variables"""
+    credential = DefaultAzureCredential()
+    kv_client = SecretClient(vault_url=vault_url, credential=credential)
+
+    secret_names = [
+        "MCP-SERVER-URL",
+        "MCP-SERVER-LABEL", 
+        "AZURE-AI-PROJECT-ENDPOINT",
+        "AZURE-AI-MODEL-DEPLOYMENT-NAME"
+    ]
+
+    print("Loading secrets from Key Vault...")
+    for secret_name in secret_names:
+        try:
+            secret = kv_client.get_secret(secret_name)
+            env_var_name = secret_name.replace("-", "_")
+            os.environ[env_var_name] = secret.value
+        except Exception as e:
+            print(f"Error loading secret {secret_name}: {e}")
+
+    print("Environment variables loaded successfully!")

labs/ai-foundry-private-mcp/agent/sample_agents_mcp.py

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+import os, time
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+from azure.ai.agents.models import (
+    ListSortOrder,
+    McpTool,
+    RequiredMcpToolCall,
+    RunStepActivityDetails,
+    SubmitToolApprovalAction,
+    ToolApproval,
+)
+from load_env_from_kv import load_secrets_from_keyvault
+
+def run_agent(key_vault_url):
+    """Run the MCP agent with the given Key Vault URL"""
+
+    # Load secrets from Key Vault
+    if key_vault_url:
+        load_secrets_from_keyvault(key_vault_url)
+    else:
+        raise Exception("KEY_VAULT_URL is required")
+
+    # Get MCP server configuration from environment variables
+    mcp_server_url = os.environ.get("MCP_SERVER_URL")
+    mcp_server_label = os.environ.get("MCP_SERVER_LABEL")
+
+    project_client = AIProjectClient(
+        endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        credential=DefaultAzureCredential(),
+    )
+
+    mcp_tool = McpTool(
+        server_label=mcp_server_label,
+        server_url=mcp_server_url,
+        allowed_tools=[],  
+    )
+
+    # Get access token using managed identity
+    credential = DefaultAzureCredential()
+    access_token = credential.get_token("https://azure-api.net/authorization-manager/.default").token
+    print("Obtained access token for MCP server.")
+
+    # Create agent with MCP tool and process agent run
+    with project_client:
+        agents_client = project_client.agents
+
+        # Create a new agent
+        agent = agents_client.create_agent(
+            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            name="my-mcp-agent",
+            instructions="You are a helpful agent that can use MCP tools to assist users. Use the available MCP tools to answer questions and perform tasks.",
+            tools=mcp_tool.definitions,
+        )
+
+        print(f"Created agent, ID: {agent.id}")
+        print(f"MCP Server: {mcp_tool.server_label} at {mcp_tool.server_url}")
+
+        # Create thread for communication
+        thread = agents_client.threads.create()
+        print(f"Created thread, ID: {thread.id}")
+
+        # Create message to thread
+        message = agents_client.messages.create(
+            thread_id=thread.id,
+            role="user",
+            content="Order sku-123 with 5 items",
+        )
+        print(f"Created message, ID: {message.id}")
+
+        # Create and process agent run in thread with MCP tools
+        mcp_tool.update_headers("Authorization", f"Bearer {access_token}")
+        run = agents_client.runs.create(thread_id=thread.id, agent_id=agent.id, tool_resources=mcp_tool.resources)
+        print(f"Created run, ID: {run.id}")
+
+        while run.status in ["queued", "in_progress", "requires_action"]:
+            time.sleep(0.1)
+            run = agents_client.runs.get(thread_id=thread.id, run_id=run.id)
+
+            if run.status == "requires_action" and isinstance(run.required_action, SubmitToolApprovalAction):
+                tool_calls = run.required_action.submit_tool_approval.tool_calls
+                if not tool_calls:
+                    print("No tool calls provided - cancelling run")
+                    agents_client.runs.cancel(thread_id=thread.id, run_id=run.id)
+                    break
+
+                tool_approvals = []
+                for tool_call in tool_calls:
+                    if isinstance(tool_call, RequiredMcpToolCall):
+                        try:
+                            print(f"Approving tool call: {tool_call}")
+                            tool_approvals.append(
+                                ToolApproval(
+                                    tool_call_id=tool_call.id,
+                                    approve=True,
+                                    headers=mcp_tool.headers,
+                                )
+                            )
+                        except Exception as e:
+                            print(f"Error approving tool_call {tool_call.id}: {e}")
+
+                if tool_approvals:
+                    agents_client.runs.submit_tool_outputs(
+                        thread_id=thread.id, run_id=run.id, tool_approvals=tool_approvals
+                    )
+
+            print(f"Current run status: {run.status}")
+
+        print(f"Run completed with status: {run.status}")
+        if run.status == "failed":
+            print(f"Run failed: {run.last_error}")
+
+        # Display run steps and tool calls
+        run_steps = agents_client.run_steps.list(thread_id=thread.id, run_id=run.id)
+
+        # Loop through each step
+        for step in run_steps:
+            print(f"Step {step['id']} status: {step['status']}")
+
+            # Check if there are tool calls in the step details
+            step_details = step.get("step_details", {})
+            tool_calls = step_details.get("tool_calls", [])
+
+            if tool_calls:
+                print("  MCP Tool calls:")
+                for call in tool_calls:
+                    print(f"    Tool Call ID: {call.get('id')}")
+                    print(f"    Type: {call.get('type')}")
+
+            if isinstance(step_details, RunStepActivityDetails):
+                for activity in step_details.activities:
+                    for function_name, function_definition in activity.tools.items():
+                        print(
+                            f'  The function {function_name} with description "{function_definition.description}" will be called.:'
+                        )
+                        if len(function_definition.parameters) > 0:
+                            print("  Function parameters:")
+                            for argument, func_argument in function_definition.parameters.properties.items():
+                                print(f"      {argument}")
+                                print(f"      Type: {func_argument.type}")
+                                print(f"      Description: {func_argument.description}")
+                        else:
+                            print("This function has no parameters")
+
+            print()  # add an extra newline between steps
+
+        # Fetch and log all messages
+        messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
+        print("\nConversation:")
+        print("-" * 50)
+        for msg in messages:
+            if msg.text_messages:
+                last_text = msg.text_messages[-1]
+                print(f"{msg.role.upper()}: {last_text.text.value}")
+                print("-" * 50)
+
+        # Example of dynamic tool management
+        print(f"\nDemonstrating dynamic tool management:")
+        print(f"Current allowed tools: {mcp_tool.allowed_tools}")
+
+        # Clean-up and delete the agent once the run is finished
+        agents_client.delete_agent(agent.id)
+        print("Deleted agent")
+
+
+if __name__ == "__main__":
+    import sys
+
+    # Get Key Vault URL from command line argument or environment variable
+    if len(sys.argv) > 1:
+        key_vault_url = sys.argv[1]
+    else:
+        key_vault_url = os.environ.get("KEY_VAULT_URL")
+
+    if not key_vault_url:
+        print("Error: KEY_VAULT_URL must be provided as argument or environment variable")
+        print("Usage: python sample_agents_mcp.py <key-vault-url>")
+        sys.exit(1)
+
+    print(f"Using Key Vault: {key_vault_url}")
+    run_agent(key_vault_url)

labs/ai-foundry-private-mcp/bicepconfig.json

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+{
+  "experimentalFeaturesEnabled": {
+    "extensibility": true
+  },
+  "extensions": {
+    "microsoftGraphV1": "br:mcr.microsoft.com/bicep/extensions/microsoftgraph/v1.0:0.2.0-preview"
+  }
+}