用 ADK 使用Cloud Run的MCP

已經在Cloud Run 上架好了安全又方便的MCP ，那就來給 ADK Agent 使用吧 !

實作範例

上一篇已經完成建立的 MCP 伺服器

授予 Cloud Run 服務身分呼叫遠端 MCP 伺服器的權限

gcloud projects add-iam-policy-binding $PROJECT_ID \
  --member="serviceAccount:$SERVICE_ACCOUNT" \
  --role="roles/run.invoker"

將MCP 伺服器網址儲存至環境變數

echo -e "\nMCP_SERVER_URL=https://zoo-mcp-server-${PROJECT_NUMBER}.europe-west1.run.app/mcp/" >> .env

使用公開 MCP 伺服器連結

echo -e "\nMCP_SERVER_URL=https://zoo-mcp-server-${PROJECT_NUMBER}.europe-west1.run.app/mcp/" >> .env

建立代理程式工作流程

建立 init.py 檔案
cloudshell edit init.py
內容

from . import agent

建立 main agent.py 檔案

cloudshell edit agent.py

agent.py的內容

import os
import logging
import google.cloud.logging
from dotenv import load_dotenv

from google.adk import Agent
from google.adk.agents import SequentialAgent
from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, StreamableHTTPConnectionParams
from google.adk.tools.tool_context import ToolContext
from google.adk.tools.langchain_tool import LangchainTool

from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper

import google.auth
import google.auth.transport.requests
import google.oauth2.id_token
import collections.abc

# --- Setup Logging and Environment ---

cloud_logging_client = google.cloud.logging.Client()
cloud_logging_client.setup_logging()

load_dotenv()

model_name = os.getenv("MODEL")

# Greet user and save their prompt

def add_prompt_to_state(
    tool_context: ToolContext, prompt: str
) -> dict[str, str]:
    """Saves the user's initial prompt to the state."""
    tool_context.state["PROMPT"] = prompt
    logging.info(f"[State updated] Added to PROMPT: {prompt}")
    return {"status": "success"}


# Configuring the MCP Tool to connect to the Zoo MCP server

mcp_server_url = os.getenv("MCP_SERVER_URL")
if not mcp_server_url:
    raise ValueError("The environment variable MCP_SERVER_URL is not set.")

def get_id_token():
    """Get an ID token to authenticate with the MCP server."""
    target_url = os.getenv("MCP_SERVER_URL")
    audience = target_url.split('/mcp/')[0]
    request = google.auth.transport.requests.Request()
    id_token = google.oauth2.id_token.fetch_id_token(request, audience)
    return id_token

"""
# Use this code if you are using the public MCP Server and comment out the code below defining mcp_tools
mcp_tools = MCPToolset(
    connection_params=StreamableHTTPConnectionParams(
        url=mcp_server_url
    )
)
"""

class _LazyAuthHeader(collections.abc.Mapping):
    """一個在需要時才動態獲取新 ID token 的類別，用於 Authorization 標頭。"""

    def __getitem__(self, key):
        if key == "Authorization":
            logging.info("正在為 MCP 伺服器獲取新的 ID token。")
            return f"Bearer {get_id_token()}"
        raise KeyError(f"在延遲認證標頭中找不到 '{key}'。")

    def __iter__(self):
        yield "Authorization"

    def __len__(self):
        return 1

mcp_tools = MCPToolset(
    connection_params=StreamableHTTPConnectionParams(
        url=mcp_server_url,
        headers=_LazyAuthHeader(),
    ),
)

# Configuring the Wikipedia Tool
wikipedia_tool = LangchainTool(
    tool=WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
)

# 1. Researcher Agent
comprehensive_researcher = Agent(
    name="comprehensive_researcher",
    model=model_name,
    description="The primary researcher that can access both internal zoo data and external knowledge from Wikipedia.",
    instruction="""
    You are a helpful research assistant. Your goal is to fully answer the user's PROMPT.
    You have access to two tools:
    1. A tool for getting specific data about animals AT OUR ZOO (names, ages, locations).
    2. A tool for searching Wikipedia for general knowledge (facts, lifespan, diet, habitat).

    First, analyze the user's PROMPT.
    - If the prompt can be answered by only one tool, use that tool.
    - If the prompt is complex and requires information from both the zoo's database AND Wikipedia,
      you MUST use both tools to gather all necessary information.
    - Synthesize the results from the tool(s) you use into preliminary data outputs.

    PROMPT:
    {{ PROMPT }}
    """,
    tools=[
        mcp_tools,
        wikipedia_tool
    ],
    output_key="research_data" # A key to store the combined findings
)

# 2. Response Formatter Agent
response_formatter = Agent(
    name="response_formatter",
    model=model_name,
    description="Synthesizes all information into a friendly, readable response.",
    instruction="""
    You are the friendly voice of the Zoo Tour Guide. Your task is to take the
    RESEARCH_DATA and present it to the user in a complete and helpful answer.

    - First, present the specific information from the zoo (like names, ages, and where to find them).
    - Then, add the interesting general facts from the research.
    - If some information is missing, just present the information you have.
    - Be conversational and engaging.

    RESEARCH_DATA:
    {{ research_data }}
    """
)

tour_guide_workflow = SequentialAgent(
    name="tour_guide_workflow",
    description="The main workflow for handling a user's request about an animal.",
    sub_agents=[
        comprehensive_researcher, # Step 1: Gather all data
        response_formatter,       # Step 2: Format the final response
    ]
)

root_agent = Agent(
    name="greeter",
    model=model_name,
    description="The main entry point for the Zoo Tour Guide.",
    instruction="""
    - Let the user know you will help them learn about the animals we have in the zoo.
    - When the user responds, use the 'add_prompt_to_state' tool to save their response.
    After using the tool, transfer control to the 'tour_guide_workflow' agent.
    """,
    tools=[add_prompt_to_state],
    sub_agents=[tour_guide_workflow]
)

準備部署應用程式

source .env

# Grant the "Vertex AI User" role to your service account
gcloud projects add-iam-policy-binding $PROJECT_ID \
  --member="serviceAccount:$SERVICE_ACCOUNT" \
  --role="roles/aiplatform.user"

使用 ADK CLI 部署代理程式

# Run the deployment command
uvx --from google-adk \
adk deploy cloud_run \
  --project=$PROJECT_ID \
  --region=europe-west1 \
  --service_name=zoo-tour-guide \
  --with_ui \
  . \
  -- \
  --labels=dev-tutorial=codelab-adk

提供已部署 Cloud Run 服務的網址。
https://zoo-tour-guide-58023433342.europe-west1.run.app/

執行結果

總結

MCP、本地 MCP、Cloud Run MCP 的差異：

No MCP：整合困難、無法擴展、安全性差。

Local MCP：標準化了，但部署和擴展有限。

Cloud Run MCP：在整合、部署、自動伸縮、安全性、可重用性上都達到最佳。

![https://ithelp.ithome.com.tw/upload/images/20250923/201216432JSvRleLut.png](https://ithelp.ithome.com.tw/upload/images/20250923/201216432JSvRleLut.png)

參考

https://codelabs.developers.google.com/codelabs/cloud-run/use-mcp-server-on-cloud-run-with-an-adk-agent?hl=zh_tw#0