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This commit is contained in:
2026-01-24 03:05:12 +11:00
parent f78f2388b3
commit 539852f81c
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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
<!--
Disable central package management for this project.
This project requires explicit package references with versions specified inline rather than
inheriting them from Directory.Packages.props. This is necessary because a Docker image will
be created from this project, and the Docker build process only has access to this folder
and cannot access parent folders where Directory.Packages.props resides.
-->
<ManagePackageVersionsCentrally>false</ManagePackageVersionsCentrally>
<NoWarn>$(NoWarn);MEAI001;OPENAI001</NoWarn>
</PropertyGroup>
<!--
Remove analyzer PackageReference items inherited from Directory.Packages.props.
Note: ManagePackageVersionsCentrally only controls PackageVersion items, not PackageReference items.
Directory.Packages.props contains both PackageVersion and PackageReference entries for analyzers,
and the PackageReference items are always inherited through MSBuild imports regardless of the
ManagePackageVersionsCentrally setting. We must explicitly remove them before adding our own versions.
-->
<ItemGroup>
<PackageReference Remove="Microsoft.CodeAnalysis.NetAnalyzers" />
<PackageReference Remove="Microsoft.VisualStudio.Threading.Analyzers" />
<PackageReference Remove="xunit.analyzers" />
<PackageReference Remove="Moq.Analyzers" />
<PackageReference Remove="Roslynator.Analyzers" />
<PackageReference Remove="Roslynator.CodeAnalysis.Analyzers" />
<PackageReference Remove="Roslynator.Formatting.Analyzers" />
</ItemGroup>
<ItemGroup>
<PackageReference Include="Azure.AI.AgentServer.AgentFramework" Version="1.0.0-beta.5" />
<PackageReference Include="Azure.AI.OpenAI" Version="2.7.0-beta.2" />
<PackageReference Include="Azure.Identity" Version="1.17.1" />
<PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-preview.251219.1" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.1.1-preview.1.25612.2" />
</ItemGroup>
<!-- Add analyzers with compatible versions -->
<ItemGroup>
<PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Microsoft.VisualStudio.Threading.Analyzers" Version="17.14.15">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.CodeAnalysis.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
<PackageReference Include="Roslynator.Formatting.Analyzers" Version="4.14.1">
<PrivateAssets>all</PrivateAssets>
<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
</PackageReference>
</ItemGroup>
</Project>

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# Build the application
FROM mcr.microsoft.com/dotnet/sdk:10.0-alpine AS build
WORKDIR /src
# Copy files from the current directory on the host to the working directory in the container
COPY . .
RUN dotnet restore
RUN dotnet build -c Release --no-restore
RUN dotnet publish -c Release --no-build -o /app -f net10.0
# Run the application
FROM mcr.microsoft.com/dotnet/aspnet:10.0-alpine AS final
WORKDIR /app
# Copy everything needed to run the app from the "build" stage.
COPY --from=build /app .
EXPOSE 8088
ENTRYPOINT ["dotnet", "AgentWithHostedMCP.dll"]

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// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to create and use a simple AI agent with OpenAI Responses as the backend, that uses a Hosted MCP Tool.
// In this case the OpenAI responses service will invoke any MCP tools as required. MCP tools are not invoked by the Agent Framework.
// The sample demonstrates how to use MCP tools with auto approval by setting ApprovalMode to NeverRequire.
using Azure.AI.AgentServer.AgentFramework.Extensions;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI.Responses;
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// Create an MCP tool that can be called without approval.
AITool mcpTool = new HostedMcpServerTool(serverName: "microsoft_learn", serverAddress: "https://learn.microsoft.com/api/mcp")
{
AllowedTools = ["microsoft_docs_search"],
ApprovalMode = HostedMcpServerToolApprovalMode.NeverRequire
};
// Create an agent with the MCP tool using Azure OpenAI Responses.
AIAgent agent = new AzureOpenAIClient(
new Uri(endpoint),
new DefaultAzureCredential())
.GetResponsesClient(deploymentName)
.CreateAIAgent(
instructions: "You answer questions by searching the Microsoft Learn content only.",
name: "MicrosoftLearnAgent",
tools: [mcpTool]);
await agent.RunAIAgentAsync();

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# What this sample demonstrates
This sample demonstrates how to use a Hosted Model Context Protocol (MCP) server with an AI agent.
The agent connects to the Microsoft Learn MCP server to search documentation and answer questions using official Microsoft content.
Key features:
- Configuring MCP tools with automatic approval (no user confirmation required)
- Filtering available tools from an MCP server
- Using Azure OpenAI Responses with MCP tools
## Prerequisites
Before running this sample, ensure you have:
1. An Azure OpenAI endpoint configured
2. A deployment of a chat model (e.g., gpt-4o-mini)
3. Azure CLI installed and authenticated
**Note**: This sample uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource.
## Environment Variables
Set the following environment variables:
```powershell
# Replace with your Azure OpenAI endpoint
$env:AZURE_OPENAI_ENDPOINT="https://your-openai-resource.openai.azure.com/"
# Optional, defaults to gpt-4o-mini
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
```
## How It Works
The sample connects to the Microsoft Learn MCP server and uses its documentation search capabilities:
1. The agent is configured with a HostedMcpServerTool pointing to `https://learn.microsoft.com/api/mcp`
2. Only the `microsoft_docs_search` tool is enabled from the available MCP tools
3. Approval mode is set to `NeverRequire`, allowing automatic tool execution
4. When you ask questions, Azure OpenAI Responses automatically invokes the MCP tool to search documentation
5. The agent returns answers based on the Microsoft Learn content
In this configuration, the OpenAI Responses service manages tool invocation directly - the Agent Framework does not handle MCP tool calls.

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name: AgentWithHostedMCP
displayName: "Microsoft Learn Response Agent with MCP"
description: >
An AI agent that uses Azure OpenAI Responses with a Hosted Model Context Protocol (MCP) server.
The agent answers questions by searching Microsoft Learn documentation using MCP tools.
This demonstrates how MCP tools can be integrated with Azure OpenAI Responses where the service
itself handles tool invocation.
metadata:
authors:
- Microsoft Agent Framework Team
tags:
- Azure AI AgentServer
- Microsoft Agent Framework
- Model Context Protocol
- MCP
- Tool Call Approval
template:
kind: hosted
name: AgentWithHostedMCP
protocols:
- protocol: responses
version: v1
environment_variables:
- name: AZURE_OPENAI_ENDPOINT
value: ${AZURE_OPENAI_ENDPOINT}
- name: AZURE_OPENAI_DEPLOYMENT_NAME
value: gpt-4o-mini
resources:
- name: "gpt-4o-mini"
kind: model
id: gpt-4o-mini

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@host = http://localhost:8088
@endpoint = {{host}}/responses
### Health Check
GET {{host}}/readiness
### Simple string input - Ask about MCP Tools
POST {{endpoint}}
Content-Type: application/json
{
"input": "Please summarize the Azure AI Agent documentation related to MCP Tool calling?"
}
### Explicit input - Ask about Agent Framework
POST {{endpoint}}
Content-Type: application/json
{
"input": [
{
"type": "message",
"role": "user",
"content": [
{
"type": "input_text",
"text": "What is the Microsoft Agent Framework?"
}
]
}
]
}