Python: Added MCP tool support for azure functions package. (#2385)

* Python: Add Scaffolding for Durable AzureFunctions package to Agent Framework (#1823) * Add scafolding * update readme * add code owners and label * update owners * .NET: Durable extension: initial src and unit tests (#1900) * Python: Add Durable Agent Wrapper code (#1913) * add initial changes * Move code and add single sample * Update logger * Remove unused code * address PR comments * cleanup code and address comments --------- Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> * Azure Functions .NET samples (#1939) * Python: Add Unit tests for Azurefunctions package (#1976) * Add Unit tests for Azurefunctions * remove duplicate import * .NET: [Feature Branch] Migrate state schema updates and support for agents as MCP tools (#1979) * Python: Add more samples for Azure Functions (#1980) * Move all samples * fix comments * remove dead lines * Make samples simpler * Agents as MCP tools * Removed unused files and updated sample * .NET: [Feature Branch] Durable Task extension integration tests (#2017) * .NET: [Feature Branch] Update OpenAI config for integration tests (#2063) * Python: Add Integration tests for AzureFunctions (#2020) * Add Integration tests * Remove DTS extension * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Add pyi file for type safety * Add samples in readme * Updated all readme instructions * Address comments * Update readmes * Fix requirements * Address comments --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Addressed copilot feedback * Minor refactoring and added tests * Updated mcp sample * Fixed broken link in readme * Addressed copilot comments * Addressed feedback * Updated property to enable_mcp_tool_trigger --------- Co-authored-by: Laveesh Rohra <larohra@microsoft.com> Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> Co-authored-by: Chris Gillum <cgillum@microsoft.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
2026-06-16 21:04:09 +08:00 · 2025-11-25 12:50:02 -06:00
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+# 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)](https://modelcontextprotocol.io/) 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](../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:
+
+```json
+{
+  "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**:
+   ```bash
+   cd python/samples/getting_started/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](https://modelcontextprotocol.io/docs/tools/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:
+
+```bash
+# 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:
+
+```bash
+curl http://localhost:7071/api/health
+```
+
+Expected response:
+
+```json
+{
+  "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:
+
+```python
+from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
+
+# Create Azure OpenAI Chat Client
+chat_client = AzureOpenAIChatClient()
+
+# Define agents with different roles
+joker_agent = chat_client.create_agent(
+    name="Joker",
+    instructions="You are good at telling jokes.",
+)
+
+stock_agent = chat_client.create_agent(
+    name="StockAdvisor",
+    instructions="Check stock prices.",
+)
+
+plant_agent = chat_client.create_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](https://modelcontextprotocol.io/)
+- [Microsoft Agent Framework Documentation](https://github.com/microsoft/agent-framework)
+- [Azure Functions Documentation](https://learn.microsoft.com/azure/azure-functions/)
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+"""
+Example showing how to configure AI agents with different trigger configurations.
+
+This sample demonstrates how to configure 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
+- Flexible Agent Registration: Register agents with customizable trigger configurations
+
+This sample creates three agents with different trigger configurations:
+- Joker: HTTP trigger only (default)
+- StockAdvisor: MCP tool trigger only (HTTP disabled)
+- PlantAdvisor: Both HTTP and MCP tool triggers enabled
+
+Required environment variables:
+- AZURE_OPENAI_ENDPOINT: Your Azure OpenAI endpoint
+- AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: Your Azure OpenAI deployment name
+
+Authentication uses AzureCliCredential (Azure Identity).
+"""
+
+from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
+
+# Create Azure OpenAI Chat Client
+# This uses AzureCliCredential for authentication (requires 'az login')
+chat_client = AzureOpenAIChatClient()
+
+# Define three AI agents with different roles
+# Agent 1: Joker - HTTP trigger only (default)
+agent1 = chat_client.create_agent(
+    name="Joker",
+    instructions="You are good at telling jokes.",
+)
+
+# Agent 2: StockAdvisor - MCP tool trigger only
+agent2 = chat_client.create_agent(
+    name="StockAdvisor",
+    instructions="Check stock prices.",
+)
+
+# Agent 3: PlantAdvisor - Both HTTP and MCP tool triggers
+agent3 = chat_client.create_agent(
+    name="PlantAdvisor",
+    instructions="Recommend plants.",
+    description="Get plant recommendations.",
+)
+
+# Create the AgentFunctionApp with selective trigger configuration
+app = AgentFunctionApp(
+    enable_health_check=True,
+)
+
+# Agent 1: HTTP trigger only (default)
+app.add_agent(agent1)
+
+# Agent 2: Disable HTTP trigger, enable MCP tool trigger only
+app.add_agent(agent2, enable_http_endpoint=False, enable_mcp_tool_trigger=True)
+
+# Agent 3: Enable both HTTP and MCP tool triggers
+app.add_agent(agent3, enable_http_endpoint=True, enable_mcp_tool_trigger=True)
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+{
+  "version": "2.0",
+  "extensionBundle": {
+    "id": "Microsoft.Azure.Functions.ExtensionBundle",
+    "version": "[4.*, 5.0.0)"
+  }
+}
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+{
+  "IsEncrypted": false,
+  "Values": {
+    "FUNCTIONS_WORKER_RUNTIME": "python",
+    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
+    "DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
+    "AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
+    "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME": "<AZURE_OPENAI_CHAT_DEPLOYMENT_NAME>"
+  }
+}
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+agent-framework-azurefunctions
+azure-identity