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68 lines
3.3 KiB
Markdown
68 lines
3.3 KiB
Markdown
# Getting started with Model Content Protocol
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The getting started with Model Content Protocol samples demonstrate how to use MCP Server tools from an agent.
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## Getting started with agents prerequisites
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Before you begin, ensure you have the following prerequisites:
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- .NET 10.0 SDK or later
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- Azure OpenAI service endpoint and deployment configured
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- Azure CLI installed and authenticated (for Azure credential authentication)
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- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource.
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**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
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**Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
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## Samples
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|Sample|Description|
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|[Agent with MCP server tools](./Agent_MCP_Server/)|This sample demonstrates how to use MCP server tools with a simple agent|
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|[Agent with MCP server tools and authorization](./Agent_MCP_Server_Auth/)|This sample demonstrates how to use MCP Server tools from a protected MCP server with a simple agent|
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|[Agent with per-run MCP authentication headers](./Agent_MCP_PerRun_AuthHeaders/)|This sample demonstrates how to attach per-run, refreshable authentication headers to MCP requests using a custom HttpClient handler and an AsyncLocal scope. Uses Microsoft Foundry (`FOUNDRY_PROJECT_ENDPOINT` / `FOUNDRY_MODEL`) rather than the Azure OpenAI variables in the prerequisites above.|
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|[Responses Agent with Hosted MCP tool](./ResponseAgent_Hosted_MCP/)|This sample demonstrates how to use the Hosted MCP tool with the Responses Service, where the service invokes any MCP tools directly|
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|[Agent with long-running MCP task (transparent polling)](./Agent_MCP_LongRunningTask_Client/)|This sample demonstrates how an agent transparently drives a long-running MCP task (SEP-2663) to completion. The wrapper polls the task internally on both `RunAsync` and `RunStreamingAsync` invocations.|
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## Running the samples from the console
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To run the samples, navigate to the desired sample directory, e.g.
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```powershell
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cd Agents_Step01_Running
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```
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Set the following environment variables:
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```powershell
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$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
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$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini" # Optional, defaults to gpt-5.4-mini
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```
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If the variables are not set, you will be prompted for the values when running the samples.
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Execute the following command to build the sample:
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```powershell
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dotnet build
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```
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Execute the following command to run the sample:
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```powershell
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dotnet run --no-build
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```
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Or just build and run in one step:
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```powershell
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dotnet run
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```
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## Running the samples from Visual Studio
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Open the solution in Visual Studio and set the desired sample project as the startup project. Then, run the project using the built-in debugger or by pressing `F5`.
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You will be prompted for any required environment variables if they are not already set.
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