agent-framework/python/samples/04-hosting/azure_functions/08_mcp_server

Agent as MCP Tool Sample

This sample demonstrates how to configure AI agents to be accessible as both HTTP endpoints and Model Context Protocol (MCP) tools, enabling flexible integration patterns for AI agent consumption.

Key Concepts Demonstrated

• Multi-trigger Agent Configuration: Configure agents to support HTTP triggers, MCP tool triggers, or both
• Microsoft Agent Framework Integration: Use the framework to define AI agents with specific roles and capabilities
• Flexible Agent Registration: Register agents with customizable trigger configurations
• MCP Server Hosting: Expose agents as MCP tools for consumption by MCP-compatible clients

Sample Architecture

This sample creates three agents with different trigger configurations:

Agent	Role	HTTP Trigger	MCP Tool Trigger	Description
Joker	Comedy specialist	✅ Enabled	❌ Disabled	Accessible only via HTTP requests
StockAdvisor	Financial data	❌ Disabled	✅ Enabled	Accessible only as MCP tool
PlantAdvisor	Indoor plant recommendations	✅ Enabled	✅ Enabled	Accessible via both HTTP and MCP

Environment Setup

See the README.md file in the parent directory for complete setup instructions, including:

• Prerequisites installation
• Azure OpenAI configuration
• Durable Task Scheduler setup
• Storage emulator configuration

Configuration

Update your local.settings.json with your Azure OpenAI credentials:

{
  "Values": {
    "AZURE_OPENAI_ENDPOINT": "https://your-resource.openai.azure.com/",
    "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME": "your-deployment-name",
    "AZURE_OPENAI_KEY": "your-api-key-if-not-using-rbac"
  }
}

Running the Sample

1. Start the Function App:

   cd python/samples/04-hosting/azure_functions/08_mcp_server
   func start
   

2. Note the MCP Server Endpoint: When the app starts, you'll see the MCP server endpoint in the terminal output. It will look like:

   MCP server endpoint:  http://localhost:7071/runtime/webhooks/mcp
   

Testing MCP Tool Integration

Using MCP Inspector

1. Install the MCP Inspector
2. Connect using the MCP server endpoint from your terminal output
3. Select "Streamable HTTP" as the transport method
4. Test the available MCP tools:
   • StockAdvisor - Available only as MCP tool
   • PlantAdvisor - Available as both HTTP and MCP tool

Using Other MCP Clients

Any MCP-compatible client can connect to the server endpoint and utilize the exposed agent tools. The agents will appear as callable tools within the MCP protocol.

Testing HTTP Endpoints

For agents with HTTP triggers enabled (Joker and PlantAdvisor), you can test them using curl:

# Test Joker agent (HTTP only)
curl -X POST http://localhost:7071/api/agents/Joker/run \
  -H "Content-Type: application/json" \
  -d '{"message": "Tell me a joke"}'

# Test PlantAdvisor agent (HTTP and MCP)
curl -X POST http://localhost:7071/api/agents/PlantAdvisor/run \
  -H "Content-Type: application/json" \
  -d '{"message": "Recommend an indoor plant"}'

Note: StockAdvisor does not have HTTP endpoints and is only accessible via MCP tool triggers.

Expected Output

HTTP Responses will be returned directly to your HTTP client.

MCP Tool Responses will be visible in:

• The terminal where func start is running
• Your MCP client interface
• The DTS dashboard at http://localhost:8080 (if using Durable Task Scheduler)

Health Check

Check the health endpoint to see which agents have which triggers enabled:

curl http://localhost:7071/api/health

Expected response:

{
  "status": "healthy",
  "agents": [
    {
      "name": "Joker",
      "type": "Agent",
      "http_endpoint_enabled": true,
      "mcp_tool_enabled": false
    },
    {
      "name": "StockAdvisor",
      "type": "Agent",
      "http_endpoint_enabled": false,
      "mcp_tool_enabled": true
    },
    {
      "name": "PlantAdvisor",
      "type": "Agent",
      "http_endpoint_enabled": true,
      "mcp_tool_enabled": true
    }
  ],
  "agent_count": 3
}

Code Structure

The sample shows how to enable MCP tool triggers with flexible agent configuration:

from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient

# Create Azure OpenAI Chat Client
client = AzureOpenAIChatClient()

# Define agents with different roles
joker_agent = client.as_agent(
    name="Joker",
    instructions="You are good at telling jokes.",
)

stock_agent = client.as_agent(
    name="StockAdvisor",
    instructions="Check stock prices.",
)

plant_agent = client.as_agent(
    name="PlantAdvisor",
    instructions="Recommend plants.",
    description="Get plant recommendations.",
)

# Create the AgentFunctionApp
app = AgentFunctionApp(enable_health_check=True)

# Configure agents with different trigger combinations:
# HTTP trigger only (default)
app.add_agent(joker_agent)

# MCP tool trigger only (HTTP disabled)
app.add_agent(stock_agent, enable_http_endpoint=False, enable_mcp_tool_trigger=True)

# Both HTTP and MCP tool triggers enabled
app.add_agent(plant_agent, enable_http_endpoint=True, enable_mcp_tool_trigger=True)

This automatically creates the following endpoints based on agent configuration:

• POST /api/agents/{AgentName}/run - HTTP endpoint (when enable_http_endpoint=True)
• MCP tool triggers for agents with enable_mcp_tool_trigger=True
• GET /api/health - Health check endpoint showing agent configurations

Learn More

• Model Context Protocol Documentation
• Microsoft Agent Framework Documentation
• Azure Functions Documentation