modelscope·@railwayapp/railway-mcp-server
暂无描述。
A Model Context Protocol (MCP) server for interacting with your Railway account. This is a local MCP server provides a set of opinionated workflows and tools for managing Railway resources.
[!IMPORTANT] The MCP server doesn't include destructive actions by design, that said, you should still keep an eye on which tools and commands are being executed.
The Railway CLI is required for this server to function.
You can add the Railway MCP Server to Cursor by clicking the button below.
Alternatively, you can add the following configuration to .cursor/mcp.json
{
"mcpServers": {
"railway-mcp-server": {
"command": "npx",
"args": ["-y", "@railway/mcp-server"]
}
}
}
Add the following configuration to .vscode/mcp.json
{
"servers": {
"railway-mcp-server": {
"type": "stdio",
"command": "npx",
"args": ["-y", "@railway/mcp-server"]
}
}
}
claude mcp add railway-mcp-server -- npx -y @railway/mcp-server
Creating a new project, deploying it, and generating a domain
Create a Next.js app in this directory and deploy it to Railway. Make sure to also assign it a domain. Since we're starting from scratch, there is no need to pull information about the deployment or build for now
Deploy a from a template (database, queue, etc.). Based on your prompt, the appropriate template will be selected and deployed. In case of multiple templates, the agent will pick the most appropriate one. Writing a detailed prompt will lead to a better selection. Check out all of the available templates.
Deploy a Postgres database
Deploy a single node Clickhouse database
Pulling environment variables
I would like to pull environment variables for my project and save them in a .env file
Creating a new environment and setting it as the current linked environment
I would like to create a new development environment called `development` where I can test my changes. This environment should duplicate production. Once the environment is created, I want to set it as my current linked environment
The MCP server automatically detects your Railway CLI version to use the appropriate features.
The Railway MCP Server provides the following tools for managing your Railway infrastructure:
check-railway-status - Checks that the Railway CLI is installed and that the user is logged inlist-projects - List all Railway projectscreate-project-and-link - Create a new project and link it to the current directorylist-services - List all services in a projectlink-service - Link a service to the current directorydeploy - Deploy a servicedeploy-template - Deploy a template from the Railway Template Librarycreate-environment - Create a new environmentlink-environment - Link an environment to the current directorylist-variables - List environment variablesset-variables - Set environment variablesgenerate-domain - Generate a railway.app domain for a projectget-logs - Retrieve build or deployment logs for a service
lines parameter to limit output and filter parameter for searching logsClone the repository
git clone https://github.com/railwayapp/railway-mcp-server.git
cd railway-mcp-server
Install dependencies
pnpm install
Start the development server
pnpm dev
This command will generate a build under dist/ and automatically rebuild after making changes.
Configure your MCP client
Add the following configuration to your MCP client (e.g., Cursor, VSCode) and replace /path/to/railway-mcp-server/dist/index.js with the actual path to your built server.
Cursor: .cursor/mcp.json
{
"mcpServers": {
"railway-mcp-server": {
"command": "node",
"args": ["/path/to/railway-mcp-server/dist/index.js"]
}
}
}
VSCode: .vscode/mcp.json
{
"servers": {
"railway-mcp-server": {
"type": "stdio",
"command": "node",
"args": ["/path/to/railway-mcp-server/dist/index.js"]
}
}
}
For Claude Code:
claude mcp add railway-mcp-server node /path/to/railway-mcp-server/railway-mcp-server/dist/index.js
通过专门的代理、丰富的资源和强大的工具,促进涵盖云、人工智能和区块链等不同架构领域的全面架构设计和评估。
一个将 Claude Desktop 和其他 MCP 客户端连接到 Cloudflare Workers 的软件包,通过模型上下文协议(Model Context Protocol), enables 自定义功能可以通过自然语言访问。
一种连接到 Kubernetes 集群的服务器,它通过自然语言命令启用管理功能,支持列出资源、创建/删除 Pod 以及安装 Helm 图表等操作。
一种模型上下文协议服务器, enables 人工智能代理通过标准化接口与阿里云DataWorks进行交互,从而通过DataWorks开放API无缝管理DataWorks资源和操作。
一种模型上下文协议服务器,通过 Graph API 实现与 Microsoft 365 服务(Excel、日历、邮件、OneDrive、Teams 等)的交互,使人工智能助手能够通过自然语言管理 Microsoft 365 资源。
一台使大型语言模型能够通过模型上下文协议发现并与由 OpenAPI 规范定义的 REST API 交互的服务器。