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94 lines
3.8 KiB
Markdown
94 lines
3.8 KiB
Markdown
# Agent as MCP Tool Sample
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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.
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## Key Concepts Demonstrated
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- **Multi-trigger Agent Configuration**: Configure agents to support HTTP triggers, MCP tool triggers, or both
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- **Microsoft Agent Framework Integration**: Use the framework to define AI agents with specific roles and capabilities
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- **Flexible Agent Registration**: Register agents with customizable trigger configurations
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- **MCP Server Hosting**: Expose agents as MCP tools for consumption by MCP-compatible clients
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## Sample Architecture
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This sample creates three agents with different trigger configurations:
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| Agent | Role | HTTP Trigger | MCP Tool Trigger | Description |
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|-------|------|--------------|------------------|-------------|
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| **Joker** | Comedy specialist | ✅ Enabled | ❌ Disabled | Accessible only via HTTP requests |
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| **StockAdvisor** | Financial data | ❌ Disabled | ✅ Enabled | Accessible only as MCP tool |
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| **PlantAdvisor** | Indoor plant recommendations | ✅ Enabled | ✅ Enabled | Accessible via both HTTP and MCP |
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## Environment Setup
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See the [README.md](../README.md) file in the parent directory for complete setup instructions, including:
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- Prerequisites installation
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- Azure OpenAI configuration
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- Durable Task Scheduler setup
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- Storage emulator configuration
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For this sample, you'll also need to install [node.js](https://nodejs.org/en/download) in order to use the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector) tool.
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## Configuration
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Update your `local.settings.json` with your Azure OpenAI credentials:
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```json
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{
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"Values": {
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"AZURE_OPENAI_ENDPOINT": "https://your-resource.openai.azure.com/",
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"AZURE_OPENAI_DEPLOYMENT": "your-deployment-name",
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"AZURE_OPENAI_KEY": "your-api-key-if-not-using-rbac"
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}
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}
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```
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## Running the Sample
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1. **Start the Function App**:
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```bash
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cd dotnet/samples/AzureFunctions/07_AgentAsMcpTool
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func start
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```
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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:
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```text
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MCP server endpoint: http://localhost:7071/runtime/webhooks/mcp
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```
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## Testing MCP Tool Integration
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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.
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### Using MCP Inspector
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1. Run the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector) from the command line:
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```bash
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npx @modelcontextprotocol/inspector
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```
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1. Connect using the MCP server endpoint from your terminal output
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- For **Transport Type**, select **"Streamable HTTP"**
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- For **URL**, enter the MCP server endpoint `http://localhost:7071/runtime/webhooks/mcp`
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- Click the **Connect** button
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1. Click the **List Tools** button to see the available MCP tools. You should see the `StockAdvisor` and `PlantAdvisor` tools.
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1. Test the available MCP tools:
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- **StockAdvisor** - Set "MSFT ATH" (ATH is "all time high") as the query and click the **Run Tool** button.
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- **PlantAdvisor** - Set "Low light in Seattle" as the query and click the **Run Tool** button.
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You'll see the results of the tool calls in the MCP Inspector interface under the **Tool Results** section. You should also see the results in the terminal where you ran the `func start` command.
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## Learn More
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- [Model Context Protocol Documentation](https://modelcontextprotocol.io/)
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- [Microsoft Agent Framework Documentation](https://github.com/Azure/durable-agent-framework)
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- [Azure Functions Documentation](https://learn.microsoft.com/azure/azure-functions/)
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