Microsoft Foundry 工具箱

Microsoft Foundry 工具箱是托管工具配置的命名、版本控制的服务器端捆绑包，例如代码解释器、文件搜索、图像生成、MCP 和 Web 搜索。工具箱允许你在 Foundry 中管理工具配置一次，并在代理之间重复使用它。

代理框架涵盖工具箱消耗。通过 Foundry 门户或 azure-ai-projects SDK 创建和更新工具箱版本。

Important

FoundryToolbox 由 beta agent-framework-foundry-hosting 包提供，可以在稳定发布之前进行更改。

对于服务托管 FoundryAgent，将工具箱附加到 Foundry 中的代理定义。目前未记录客户端.NET工具箱使用指南。

安装软件包

pip install agent-framework-foundry-hosting agent-framework-foundry --pre

FoundryToolbox是从 .agent_framework.foundryagent-framework-foundry-hosting.

配置工具箱

设置显式工具箱 MCP 终结点：

TOOLBOX_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>/toolboxes/<name>/mcp?api-version=v1"

或者让 FoundryToolbox 构造终结点：

FOUNDRY_PROJECT_ENDPOINT="https://<account>.services.ai.azure.com/api/projects/<project>"
TOOLBOX_NAME="<toolbox-name>"

托管代理示例也用于 AZURE_AI_MODEL_DEPLOYMENT_NAMEFoundryChatClient。

`FoundryToolbox`与托管代理一起使用

FoundryToolbox解析其终结点，使用提供的Azure凭据对每个 MCP 请求进行身份验证，转发 Foundry 每请求调用 ID，并参与代理的连接生命周期。

import asyncio
import os

from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient, FoundryToolbox, ResponsesHostServer
from azure.identity import DefaultAzureCredential
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()


async def main():
    credential = DefaultAzureCredential()

    # FoundryToolbox resolves the toolbox endpoint from the environment
    # (TOOLBOX_ENDPOINT, or FOUNDRY_PROJECT_ENDPOINT + TOOLBOX_NAME), authenticates
    # every request with the credential, and transparently forwards the platform
    # per-request call-id to the toolbox. The hosting server enters the agent, which
    # connects the toolbox on first use and closes it at shutdown.
    toolbox = FoundryToolbox(credential)

    # Create the chat client
    client = FoundryChatClient(
        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
        credential=credential,
    )

    agent = Agent(
        client=client,
        instructions="You are a friendly assistant. Keep your answers brief.",
        tools=toolbox,
        # History will be managed by the hosting infrastructure, thus there
        # is no need to store history by the service. Learn more at:
        # https://developers.openai.com/api/reference/resources/responses/methods/create
        default_options={"store": False},
    )

    server = ResponsesHostServer(agent)
    await server.run_async()

公开工具箱技能

工具箱可以通过 MCP 公开代理技能。仅当技能应为模型可见时设置 load_tools=False ，然后将工具箱添加为工具，以便其 MCP 会话连接并用作 as_skills_provider() 上下文提供程序。

import asyncio
import os

from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient, FoundryToolbox, ResponsesHostServer
from azure.identity import DefaultAzureCredential
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()


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

    # FoundryToolbox resolves the toolbox endpoint from the environment
    # (TOOLBOX_ENDPOINT, or FOUNDRY_PROJECT_ENDPOINT + TOOLBOX_NAME), authenticates
    # every request with the credential, and forwards the platform per-request
    # call-id. ``load_tools=False`` keeps the toolbox's tools hidden so only its
    # Agent Skills (SEP-2640) are surfaced; passing it via ``tools=`` connects the
    # MCP session that ``as_skills_provider()`` reads from.
    toolbox = FoundryToolbox(credential, load_tools=False)

    # as_skills_provider() discovers skills from skill://index.json on the toolbox
    # MCP session and exposes them as an agent context provider; SKILL.md bodies are
    # fetched on demand via resources/read. disable_load_skill_approval=True registers
    # the load_skill tool with approval_mode="never_require" so this unattended agent
    # can load skills without an approval round-trip -- the Responses host runs the
    # agent without an AgentSession, which the default approval flow requires.
    skills_provider = toolbox.as_skills_provider(disable_load_skill_approval=True)

    client = FoundryChatClient(
        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
        credential=credential,
    )

    agent = Agent(
        client=client,
        name=os.environ.get("AGENT_NAME", "hosted-toolbox-mcp-skills"),
        instructions="You are a helpful assistant.",
        tools=toolbox,
        context_providers=[skills_provider],
        # History will be managed by the hosting infrastructure, thus there
        # is no need to store history by the service. Learn more at:
        # https://developers.openai.com/api/reference/resources/responses/methods/create
        default_options={"store": False},
    )

    server = ResponsesHostServer(agent)
    await server.run_async()

默认情况下，技能操作仍启用审批。仅对受信任的无人参与方案禁用单个审批。

将工具箱与 `FoundryAgent`

将工具箱附加到 Foundry 中的提示或托管代理定义。 FoundryAgent 使用存储的工具配置;传递工具箱客户端不会将其添加到托管代理。

通过原始 MCP 进行连接

当应用程序不使用宿主包装器时直接使用MCPStreamableHTTPToolFoundryToolbox。通过提供工具箱终结点和Entra ID持有者令牌header_provider。

import asyncio
import os
from collections.abc import Callable
from typing import Any, cast

from agent_framework import Agent, MCPStreamableHTTPTool
from agent_framework.foundry import FoundryChatClient
from azure.core.credentials import TokenCredential
from azure.identity import AzureCliCredential, DefaultAzureCredential, get_bearer_token_provider
from dotenv import load_dotenv
def make_toolbox_header_provider(credential: TokenCredential) -> Callable[[dict[str, Any]], dict[str, str]]:
    """Build a header_provider that injects a fresh Azure AI bearer token on every MCP request."""
    get_token = get_bearer_token_provider(credential, "https://ai.azure.com/.default")

    def provide(_kwargs: dict[str, Any]) -> dict[str, str]:
        return {
            "Authorization": f"Bearer {get_token()}",
        }

    return provide


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

    toolbox_tool = MCPStreamableHTTPTool(
        name="foundry_toolbox",
        description="Tools exposed by the configured Foundry toolbox",
        url=os.environ["FOUNDRY_TOOLBOX_ENDPOINT"],
        header_provider=make_toolbox_header_provider(credential),
        load_prompts=False,
    )

    async with Agent(
        client=FoundryChatClient(
            project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
            model=os.environ["FOUNDRY_MODEL"],
            credential=credential,
        ),
        instructions="You are a helpful assistant. Use the available toolbox tools to answer the user.",
        tools=toolbox_tool,
    ) as agent:
        query = "What tools do you have access to?"
        print(f"User: {query}")
        result = await agent.run(query)
        print(f"Assistant: {result}")

较低级别的示例使用 FOUNDRY_TOOLBOX_ENDPOINT。工具箱技能示例使用 FOUNDRY_TOOLBOX_MCP_SERVER_URL;这些名称属于这些示例，独立于 FoundryToolbox 类 TOOLBOX_ENDPOINT 和 TOOLBOX_NAME 设置。

Limitations

工具箱中的 MCP 工具通过 Foundry project_connection_id使用服务器端身份验证;代理框架客户端不保存上游 MCP 持有者令牌。
使用工具箱作为 MCP 服务器需要对工具箱终结点进行客户端Entra ID身份验证。
同意流响应（例如 CONSENT_REQUIRED ，在代理运行时处理），而不是在创建工具箱连接时处理。

Samples

Sample	Description
foundry_toolbox/main.py	`FoundryToolbox` 使用托管响应代理
foundry_toolbox_mcp_skills/main.py	工具箱支持的代理技能
foundry_chat_client_with_toolbox.py	使用 `MCPStreamableHTTPTool` 的工具箱 MCP 使用量
foundry_chat_client_with_toolbox_skills.py	由工具箱支持的技能配置
invoke_foundry_toolbox_mcp	工作流端 MCP 消耗

Go 当前不公开 Foundry 工具箱帮助程序。通过 Foundry 配置工具箱，并使用 Go 代理支持的本地或托管工具声明。

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Last updated on 2026-08-13