Cloud Run MCP Server
Enables MCP-compatible AI agents to deploy applications to Google Cloud Run by providing tools for deploying code, listing services, and managing Google Cloud projects.
README
MCP server to deploy code to Google Cloud Run
Enable MCP-compatible AI agents to deploy apps to Cloud Run.
"mcpServers":{
"cloud-run": {
"command": "npx",
"args": ["-y", "https://github.com/GoogleCloudPlatform/cloud-run-mcp"]
}
}
Deploy from AI-powered IDEs:
<img src="https://github.com/user-attachments/assets/9fdcec30-2b38-4362-9eb1-54cab09e99d4" width="800">
Deploy from AI assistant apps:
<img src="https://github.com/user-attachments/assets/b10f0335-b332-4640-af38-ea015b46b57c" width="800">
Deploy from agent SDKs, like the Google Gen AI SDK or Agent Development Kit.
[!NOTE]
This is the repository of an MCP server to deploy code to Cloud Run, to learn how to host MCP servers on Cloud Run, visit the Cloud Run documentation.
Tools
deploy-file-contents: Deploys files to Cloud Run by providing their contents directly.list-services: Lists Cloud Run services in a given project and region.get-service: Gets details for a specific Cloud Run service.deploy-local-files*: Deploys files from the local file system to a Google Cloud Run service.deploy-local-folder*: Deploys a local folder to a Google Cloud Run service.list-projects*: Lists available GCP projects.create-project*: Creates a new GCP project and attach it to the first available billing account. A project ID can be optionally specified.
* only available when running locally
Use as local MCP server
Run the Cloud Run MCP server on your local machine using local Google Cloud credentials. This is best if you are using an AI-assisted IDE (e.g. Cursor) or a desktop AI application (e.g. Claude).
-
Install Node.js (LTS version recommended).
-
Install the Google Cloud SDK and authenticate with your Google account.
-
Log in to your Google Cloud account using the command:
gcloud auth login -
Set up application credentials using the command:
gcloud auth application-default login -
Update the MCP configuration file of your MCP client with the following:
"cloud-run": { "command": "npx", "args": ["-y", "https://github.com/GoogleCloudPlatform/cloud-run-mcp"] }
Use as remote MCP server
[!WARNING]
Do not use the remote MCP server without authentication. In the following instructions, we will use IAM authentication to secure the connection to the MCP server from your local machine. This is important to prevent unauthorized access to your Google Cloud resources.
Run the Cloud Run MCP server itself on Cloud Run with connection from your local machine authenticated via IAM. With this option, you will only be able to deploy code to the same Google Cloud project as where the MCP server is running.
-
Install the Google Cloud SDK and authenticate with your Google account.
-
Log in to your Google Cloud account using the command:
gcloud auth login -
Set your Google Cloud project ID using the command:
gcloud config set project YOUR_PROJECT_ID -
Deploy the Cloud Run MCP server to Cloud Run:
gcloud run deploy cloud-run-mcp --image us-docker.pkg.dev/cloudrun/container/mcp --no-allow-unauthenticatedWhen prompted, pick a region, for example
europe-west1.Note that the MCP server is not publicly accessible, it requires authentication via IAM.
-
Run a Cloud Run proxy on your local machine to connect securely using your identity to the remote MCP server running on Cloud Run:
gcloud run services proxy cloud-run-mcp --port=3000 --region=REGION --project=PROJECT_IDThis will create a local proxy on port 3000 that forwards requests to the remote MCP server and injects your identity.
-
Update the MCP configuration file of your MCP client with the following:
"cloud-run": { "url": "http://localhost:3000/sse" }If your MCP client does not support the
urlattribute, you can use mcp-remote:"cloud-run": { "command": "npx", "args": ["-y", "mcp-remote", "http://localhost:3000/sse"] }
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。