Benutzerdefiniertes Codedolmetschertool für Agents (Vorschau)

Codebeispiel

Im folgenden Python Beispiel wird gezeigt, wie Ein Agent mit einem benutzerdefinierten Codedolmetscher-MCP-Tool erstellt wird:

from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import MCPTool, MCPToolboxTool, PromptAgentDefinition

# Format: "https://resource_name.ai.azure.com/api/projects/project_name"
PROJECT_ENDPOINT = "your_project_endpoint"
MCP_SERVER_URL = "https://your-mcp-server-url"
# Optional: set to your project connection ID if your MCP server requires authentication
MCP_CONNECTION_ID = "your-mcp-connection-id"

# Create clients to call Foundry API
project = AIProjectClient(
    endpoint=PROJECT_ENDPOINT,
    credential=DefaultAzureCredential(),
)
openai = project.get_openai_client()

# Add the custom code interpreter MCP server to a toolbox. Using a toolbox is the
# recommended way to give agents tools: you curate tools once and reuse the toolbox
# across agents. See /azure/foundry/agents/concepts/toolbox-overview
toolbox = project.toolboxes.create_version(
    name="custom-code-interpreter-toolbox",
    description="Toolbox with the custom code interpreter MCP server",
    tools=[
        MCPToolboxTool(
            server_label="custom-code-interpreter",
            server_url=MCP_SERVER_URL,
            project_connection_id=MCP_CONNECTION_ID,
        )
    ],
)

# The toolbox exposes an MCP-compatible endpoint.
TOOLBOX_MCP_URL = (
    f"{PROJECT_ENDPOINT}/toolboxes/{toolbox.name}"
    f"/versions/{toolbox.version}/mcp?api-version=v1"
)

# Create a remote-tool project connection that points at the toolbox endpoint.
# Use a user Entra token so the caller's identity is passed through
# (audience https://ai.azure.com). Create the connection once, for example with
# the Azure Developer CLI:
#
#    azd ai connection create custom-code-interpreter-toolbox-conn \
#      --kind remote-tool \
#      --target "<TOOLBOX_MCP_URL>" \
#      --auth-type user-entra-token \
#      --audience https://ai.azure.com
TOOLBOX_CONNECTION_NAME = "custom-code-interpreter-toolbox-conn"

# Create an agent that uses the toolbox as an MCP tool
agent = project.agents.create_version(
    agent_name="CustomCodeInterpreterAgent",
    definition=PromptAgentDefinition(
        model="gpt-5-mini",
        instructions="You are a helpful assistant that can run Python code to analyze data and solve problems.",
        tools=[
            MCPTool(
                server_label="toolbox",
                server_url=TOOLBOX_MCP_URL,
                require_approval="never",
                project_connection_id=TOOLBOX_CONNECTION_NAME,
            )
        ],
    ),
    description="Agent with custom code interpreter for data analysis.",
)
print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")

# Test the agent with a simple calculation
response = openai.responses.create(
    input="Calculate the factorial of 10 using Python.",
    extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
)
print(f"Response: {response.output_text}")

# Clean up
project.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
project.toolboxes.delete_toolbox_version(
  toolbox_name=toolbox.name,
  version=toolbox.version,
)
print("Agent deleted")

Erwartete Ausgabe

Wenn Sie das Beispiel ausführen, wird eine Ausgabe ähnlich der folgenden angezeigt:

Agent created (id: agent-xxxxxxxxxxxx, name: CustomCodeInterpreterAgent, version: 1)
Response: The factorial of 10 is 3,628,800. I calculated this using Python's math.factorial() function.
Agent deleted

Verwenden eines gehosteten Agents

Dieses Beispiel verwendet FoundryChatClient aus dem Microsoft Agent Framework und verbindet sich über FoundryToolbox mit dem MCP-Endpunkt der Toolbox.

import asyncio

from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient, FoundryToolbox
from azure.identity import AzureCliCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import MCPToolboxTool

PROJECT_ENDPOINT = "https://<account>.services.ai.azure.com/api/projects/<project>"
MCP_SERVER_URL = "https://your-mcp-server-url"
# Optional: set to your project connection ID if your MCP server requires authentication
MCP_CONNECTION_ID = "your-mcp-connection-id"


async def main() -> None:
    credential = AzureCliCredential()

    # 1. Create the custom code interpreter MCP tool and add it to a toolbox. Using a toolbox is the
    #    recommended way to give agents tools: curate tools once and reuse the
    #    toolbox across agents. See /azure/foundry/agents/concepts/toolbox-overview
    project = AIProjectClient(endpoint=PROJECT_ENDPOINT, credential=credential)
    toolbox = project.toolboxes.create_version(
        name="custom-code-interpreter-toolbox",
        description="Toolbox with the custom code interpreter MCP server",
        tools=[
            MCPToolboxTool(
                server_label="custom-code-interpreter",
                server_url=MCP_SERVER_URL,
                project_connection_id=MCP_CONNECTION_ID,
            )
        ],
    )

    # 2. The toolbox exposes an MCP-compatible endpoint.
    TOOLBOX_MCP_URL = (
        f"{PROJECT_ENDPOINT}/toolboxes/{toolbox.name}"
        f"/versions/{toolbox.version}/mcp?api-version=v1"
    )

    # 3. Attach the toolbox to the hosted agent as an MCP tool.
, timeout=120.0)
    toolbox_tool = FoundryToolbox(credential, url=TOOLBOX_MCP_URL)

agent = Agent(
        client=FoundryChatClient(credential=credential),
        instructions="You are a helpful assistant that can run Python code to analyze data and solve problems.",
        tools=[toolbox_tool],
    )

    result = await agent.run("Calculate the factorial of 10 using Python.")
    print(result.text)


    project.toolboxes.delete_toolbox_version(
      toolbox_name=toolbox.name,
      version=toolbox.version,
    )


if __name__ == "__main__":
    asyncio.run(main())

Codebeispiel

Im folgenden C#-Beispiel wird gezeigt, wie Sie einen Agent mit einem benutzerdefinierten Codedolmetscher-MCP-Tool erstellen. Weitere Informationen zum Arbeiten mit MCP-Tools in .NET finden Sie im MCP-Toolbeispiel im Azure SDK für .NET Repository auf GitHub.

using System;
using Azure.AI.Projects;
using Azure.AI.Extensions.OpenAI;
using Azure.Identity;

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
var projectEndpoint = "your_project_endpoint";
var mcpServerUrl = "https://your-mcp-server-url";
// Optional: set to your project connection ID if your MCP server requires authentication
var mcpConnectionId = "your-mcp-connection-id";

// Create project client to call Foundry API
AIProjectClient projectClient = new(
    endpoint: new Uri(projectEndpoint),
    tokenProvider: new DefaultAzureCredential());

// Add the custom code interpreter MCP server to a toolbox. Using a toolbox is the
// recommended way to give agents tools. See /azure/foundry/agents/concepts/toolbox-overview
// Code runs in a sandboxed Azure Container Apps session.
McpTool customCodeInterpreter = ResponseTool.CreateMcpTool(
    serverLabel: "custom-code-interpreter",
    serverUri: new Uri(mcpServerUrl));
customCodeInterpreter.ProjectConnectionId = mcpConnectionId;

ToolboxVersion toolboxVersion = projectClient.AgentAdministrationClient
    .GetAgentToolboxes().CreateToolboxVersion(
        toolboxName: "custom-code-interpreter-toolbox",
        tools: [ProjectsAgentTool.AsProjectTool(customCodeInterpreter)],
        description: "Toolbox with the custom code interpreter MCP server");

// The toolbox exposes an MCP-compatible endpoint.
var toolboxMcpUrl = new Uri(
    $"{projectEndpoint}/toolboxes/{toolboxVersion.Name}" +
    $"/versions/{toolboxVersion.Version}/mcp?api-version=v1");

// Create a remote-tool project connection that points at the toolbox endpoint.
// Use a user Entra token so the caller's identity is passed through
// (audience https://ai.azure.com). Create the connection once, for example
// with the Azure Developer CLI:
//
//    azd ai connection create custom-code-interpreter-toolbox-conn \
//      --kind remote-tool \
//      --target "<toolboxMcpUrl>" \
//      --auth-type user-entra-token \
//      --audience https://ai.azure.com
var toolboxConnectionName = "custom-code-interpreter-toolbox-conn";

McpTool toolboxTool = ResponseTool.CreateMcpTool(
    serverLabel: "toolbox",
    serverUri: toolboxMcpUrl,
    toolCallApprovalPolicy: new McpToolCallApprovalPolicy(
        GlobalMcpToolCallApprovalPolicy.NeverRequireApproval));
toolboxTool.ProjectConnectionId = toolboxConnectionName;

DeclarativeAgentDefinition agentDefinition = new(model: "gpt-5-mini")
{
    Instructions = "You are a helpful assistant that can run Python code to analyze data and solve problems.",
    Tools = { toolboxTool }
};

AgentVersion agent = projectClient.AgentAdministrationClient.CreateAgentVersion(
    agentName: "CustomCodeInterpreterAgent",
    options: new(agentDefinition));

Console.WriteLine($"Agent created: {agent.Name} (version {agent.Version})");

// Create a response using the agent
ProjectResponsesClient responseClient = projectClient.ProjectOpenAIClient.GetProjectResponsesClientForAgent(agent.Name);

ResponseResult response = responseClient.CreateResponse(
    new([ResponseItem.CreateUserMessageItem("Calculate the factorial of 10 using Python.")]));

Console.WriteLine(response.GetOutputText());

// Clean up
projectClient.AgentAdministrationClient.DeleteAgentVersion(
    agentName: agent.Name,
    agentVersion: agent.Version);
Console.WriteLine("Agent deleted");

Löschen Sie die Toolboxversion, nachdem der Agent nicht mehr darauf verweist. Siehe Löschen einer Toolboxversion für den überprüften .NET Aufruf.

Erwartete Ausgabe

Agent created: CustomCodeInterpreterAgent (version 1)
The factorial of 10 is 3,628,800.
Agent deleted

Verwenden eines gehosteten Agents

In diesem Beispiel wird die Microsoft Agent Framework-Integration AddFoundryToolboxes verwendet, um den gehosteten Agent mit der Toolbox zu verbinden.

using System;
using Azure.AI.AgentServer.Responses;
using Azure.AI.AgentServer.Responses.Models;
using Azure.AI.OpenAI;
using Azure.AI.Projects;
using Azure.AI.Extensions.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry.Hosting;
using Microsoft.Extensions.DependencyInjection;
using OpenAI.Chat;

const string AgentInstructions = "You are a helpful assistant that can run Python code to analyze data and solve problems.";
const string AgentName = "CustomCodeInterpreterAgent";

string projectEndpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
    ?? "https://<account>.services.ai.azure.com/api/projects/<project>";
string openAiEndpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5-mini";
string mcpServerUrl = "https://your-mcp-server-url";
string mcpConnectionId = "your-mcp-connection-id";

DefaultAzureCredential credential = new();

// 1. Create the custom code interpreter MCP tool and add it to a toolbox. Using a toolbox is the
//    recommended way to give agents tools. See /azure/foundry/agents/concepts/toolbox-overview
AIProjectClient projectClient = new(
    endpoint: new Uri(projectEndpoint),
    tokenProvider: credential);
McpTool customCodeInterpreter = ResponseTool.CreateMcpTool(
    serverLabel: "custom-code-interpreter",
    serverUri: new Uri(mcpServerUrl));
customCodeInterpreter.ProjectConnectionId = mcpConnectionId;
ToolboxVersion toolboxVersion = projectClient.AgentAdministrationClient
    .GetAgentToolboxes().CreateToolboxVersion(
        toolboxName: "custom-code-interpreter-toolbox",
        tools: [ProjectsAgentTool.AsProjectTool(customCodeInterpreter)],
        description: "Toolbox with the custom code interpreter MCP server");

// Create the hosted agent and register the toolbox integration.
AIAgent agent = projectClient.AsAIAgent(
    model: deploymentName,
    instructions: "You are a helpful assistant with access to the toolbox tools.",
    name: "hosted-toolbox-agent");

var builder = WebApplication.CreateBuilder(args);
builder.Services.AddFoundryResponses(agent);
builder.Services.AddFoundryToolboxes(credential, toolboxVersion.Name);

var app = builder.Build();
app.MapFoundryResponses();
app.Run();

Codebeispiel

Im folgenden TypeScript-Beispiel wird gezeigt, wie Sie einen Agent mit einem benutzerdefinierten Codedolmetscher-MCP-Tool erstellen. Eine JavaScript-Version finden Sie im Beispiel zum MCP-Tool im Azure SDK für JavaScript-Repository auf GitHub.

import { DefaultAzureCredential } from "@azure/identity";
import { AIProjectClient } from "@azure/ai-projects";

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
const PROJECT_ENDPOINT = "your_project_endpoint";
const MCP_SERVER_URL = "https://your-mcp-server-url";

export async function main(): Promise<void> {
  // Create clients to call Foundry API
  const project = new AIProjectClient(PROJECT_ENDPOINT, new DefaultAzureCredential());
  const openai = project.getOpenAIClient();

  // Add the custom code interpreter MCP server to a toolbox. Using a toolbox is
  // the recommended way to give agents tools. Code runs in a sandboxed Azure
  // Container Apps session, so the tool uses require_approval: "never".
  // See /azure/foundry/agents/concepts/toolbox-overview
  const toolbox = await project.toolboxes.createVersion(
    "custom-code-interpreter-toolbox",
    [
      {
        type: "mcp",
        server_label: "custom-code-interpreter",
        server_url: MCP_SERVER_URL,
        require_approval: "never",
      },
    ],
    { description: "Toolbox with the custom code interpreter MCP server" },
  );

  // The toolbox exposes an MCP-compatible endpoint.
  const toolboxMcpUrl =
    `${PROJECT_ENDPOINT}/toolboxes/${toolbox.name}` +
    `/versions/${toolbox.version}/mcp?api-version=v1`;

  // Create a remote-tool project connection that points at the toolbox endpoint.
  // Use a user Entra token so the caller's identity is passed through
  // (audience https://ai.azure.com). Create the connection once, for example
  // with the Azure Developer CLI:
  //
  //    azd ai connection create custom-code-interpreter-toolbox-conn \
  //      --kind remote-tool \
  //      --target "<toolboxMcpUrl>" \
  //      --auth-type user-entra-token \
  //      --audience https://ai.azure.com
  const toolboxConnectionName = "custom-code-interpreter-toolbox-conn";

  // Create an agent that uses the toolbox as an MCP tool
  const agent = await project.agents.createVersion("CustomCodeInterpreterAgent", {
    kind: "prompt",
    model: "gpt-5-mini",
    instructions:
      "You are a helpful assistant that can run Python code to analyze data and solve problems.",
    tools: [
      {
        type: "mcp",
        server_label: "toolbox",
        server_url: toolboxMcpUrl,
        require_approval: "never",
        project_connection_id: toolboxConnectionName,
      },
    ],
  });
  console.log(`Agent created (name: ${agent.name}, version: ${agent.version})`);

  // Send a request to the agent
  const response = await openai.responses.create(
    {
      input: "Calculate the factorial of 10 using Python.",
    },
    {
      body: { agent_reference: { name: agent.name, type: "agent_reference" } },
    },
  );
  console.log(`Response: ${response.output_text}`);

  // Clean up
  await project.agents.deleteVersion(agent.name, agent.version);
  await project.toolboxes.deleteVersion(toolbox.name, toolbox.version);
  console.log("Agent deleted");
}

main().catch((err) => {
  console.error("The sample encountered an error:", err);
});

Erwartete Ausgabe

Agent created (name: CustomCodeInterpreterAgent, version: 1)
Response: The factorial of 10 is 3,628,800. I calculated this using Python's math.factorial() function.
Agent deleted

Fügen Sie die Abhängigkeit zu Ihrem pom.xml hinzu:

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-agents</artifactId>
    <version>2.4.0</version>
</dependency>

Codebeispiel

import com.azure.ai.agents.AgentsClient;
import com.azure.ai.agents.AgentsClientBuilder;
import com.azure.ai.agents.ResponsesClient;
import com.azure.ai.agents.models.AgentReference;
import com.azure.ai.agents.models.AgentVersionDetails;
import com.azure.ai.agents.models.AzureCreateResponseOptions;
import com.azure.ai.agents.models.McpTool;
import com.azure.ai.agents.models.PromptAgentDefinition;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.openai.models.responses.Response;
import com.openai.models.responses.ResponseCreateParams;

import java.util.Collections;

public class CustomCodeInterpreterExample {
    public static void main(String[] args) {
        // Format: "https://resource_name.ai.azure.com/api/projects/project_name"
        String projectEndpoint = "your_project_endpoint";
        String toolboxMcpUrl = projectEndpoint + "/toolboxes/custom-code-interpreter-toolbox/versions/1/mcp?api-version=v1";
        // Set to the remote-tool project connection that points at the toolbox MCP endpoint.
        String toolboxConnectionId = "custom-code-interpreter-toolbox-conn";

        // Create clients to call Foundry API
        AgentsClientBuilder builder = new AgentsClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(projectEndpoint);

        AgentsClient agentsClient = builder.buildAgentsClient();
        ResponsesClient responsesClient = builder.buildResponsesClient();

        // Attach the toolbox MCP endpoint as an MCP tool.
        // Uses require_approval: "never" because code runs in a sandboxed Container Apps session.
        McpTool toolboxTool = new McpTool("toolbox")
            .setServerUrl(toolboxMcpUrl)
            .setProjectConnectionId(toolboxConnectionId)
            .setRequireApproval("never");

        PromptAgentDefinition agentDefinition = new PromptAgentDefinition("gpt-5-mini")
            .setInstructions("You are a helpful assistant that can run Python code to analyze data and solve problems.")
            .setTools(Collections.singletonList(toolboxTool));

        AgentVersionDetails agent = agentsClient.createAgentVersion(
            "CustomCodeInterpreterAgent", agentDefinition);
        System.out.printf("Agent created: %s (version %s)%n", agent.getName(), agent.getVersion());

        // Create a response
        AgentReference agentReference = new AgentReference(agent.getName())
            .setVersion(agent.getVersion());

        Response response = responsesClient.createAzureResponse(
            new AzureCreateResponseOptions().setAgentReference(agentReference),
            ResponseCreateParams.builder()
                .input("Calculate the factorial of 10 using Python."));

        System.out.println("Response: " + response.output());

        // Clean up
        agentsClient.deleteAgentVersion(agent.getName(), agent.getVersion());
        System.out.println("Agent deleted");
    }
}

Erwartete Ausgabe

Agent created: CustomCodeInterpreterAgent (version 1)
Response: The factorial of 10 is 3,628,800.
Agent deleted

Voraussetzungen

Legen Sie diese Umgebungsvariablen fest:

• FOUNDRY_PROJECT_ENDPOINT: Ihre Projektendpunkt-URL.
• AGENT_TOKEN: Ein Bearer-Token für Foundry.

Zugriffstoken abrufen:

export AGENT_TOKEN=$(az account get-access-token --scope "https://ai.azure.com/.default" --query accessToken -o tsv)

Codebeispiel

Erstellen einer Toolbox mit dem benutzerdefinierten Codedolmetscher

Fügen Sie den benutzerdefinierten Codedolmetscher hinzu, indem Sie eine Toolbox erstellen. Fügen Sie dann die Toolbox als MCP-Tool an Ihren Agent an. Weitere Informationen finden Sie unter Was ist eine Toolbox?

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/toolboxes/custom-code-interpreter-toolbox/versions?api-version=v1" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "description": "Toolbox with the custom code interpreter MCP server",
    "tools": [
      {
        "type": "mcp",
        "server_label": "custom-code-interpreter",
        "server_url": "<MCP_SERVER_URL>",
        "project_connection_id": "<MCP_PROJECT_CONNECTION_ID>",
        "require_approval": "never"
      }
    ]
  }'

Die Toolbox stellt unter $FOUNDRY_PROJECT_ENDPOINT/toolboxes/custom-code-interpreter-toolbox/versions/<version>/mcp?api-version=v1 einen MCP-kompatiblen Endpunkt bereit, wobei <version> die vom vorherigen Aufruf zurückgegebene Version ist.

Erstellen Sie eine Verbindung mit dem Remote-Tool zur Toolbox

Erstellen Sie eine Remotetoolprojektverbindung, die auf den Toolboxendpunkt verweist. Verwenden Sie ein Benutzer-Entra-Token, damit die Identität des Anrufers (Zielgruppe https://ai.azure.com) übergeben wird:

azd ai connection create custom-code-interpreter-toolbox-conn \
  --kind remote-tool \
  --target "$FOUNDRY_PROJECT_ENDPOINT/toolboxes/custom-code-interpreter-toolbox/versions/<version>/mcp?api-version=v1" \
  --auth-type user-entra-token \
  --audience https://ai.azure.com

Erstellen eines Agents, der die Toolbox verwendet

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/agents?api-version=v1" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "name": "CustomCodeInterpreterAgent",
    "definition": {
      "kind": "prompt",
      "model": "<MODEL_DEPLOYMENT>",
      "instructions": "You are a helpful assistant that can run Python code to analyze data and solve problems.",
      "tools": [
        {
          "type": "mcp",
          "server_label": "toolbox",
          "server_url": "'$FOUNDRY_PROJECT_ENDPOINT'/toolboxes/custom-code-interpreter-toolbox/versions/<version>/mcp?api-version=v1",
          "require_approval": "never",
          "project_connection_id": "custom-code-interpreter-toolbox-conn"
        }
      ]
    }
  }'

Erstellen einer Antwort

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/openai/v1/responses" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "agent_reference": {"type": "agent_reference", "name": "CustomCodeInterpreterAgent"},
    "input": "Calculate the factorial of 10 using Python."
  }'

Bereinigen

curl -X DELETE "$FOUNDRY_PROJECT_ENDPOINT/agents/CustomCodeInterpreterAgent?api-version=v1" \
  -H "Authorization: Bearer $AGENT_TOKEN"

curl -X DELETE \
  "$FOUNDRY_PROJECT_ENDPOINT/toolboxes/custom-code-interpreter-toolbox/versions/<version>?api-version=v1" \
  -H "Authorization: Bearer $AGENT_TOKEN"

Erwartete Ausgabe

{
  "id": "resp_xxxxxxxxxxxx",
  "output": [
    {
      "type": "message",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "The factorial of 10 is 3,628,800."
        }
      ]
    }
  ]
}