Railway Integrated

Railway Integrated is an MCP server that provides seamless integration with Railway's application deployment platform, enabling AI assistants to manage projects, services, deployments, and infrastructure directly through natural language. This MCP bridge allows language models to deploy applications, inspect service logs, configure environment variables, and monitor deployment health across your Railway projects.

When to Use This MCP Server

Connect this server when...

• You manage applications on Railway and want AI assistance with deployments, rollbacks, and infrastructure configuration
• Your team uses Railway for hosting and you need conversational access to project settings, service logs, and deployment statuses
• You are building CI/CD workflows that integrate Railway deployments with your development process
• You need to manage environment variables across multiple Railway services and environments
• You want to troubleshoot deployment failures by inspecting build logs and runtime errors through AI

Consider alternatives when...

• Your applications are deployed on Heroku, Vercel, or AWS, which may have their own dedicated MCP servers
• You only need static site hosting without server-side deployment capabilities
• You require infrastructure-as-code management with tools like Pulumi or Terraform

Quick Start

Copy
# .mcp.json configuration
{
  "mcpServers": {
    "railway": {
      "command": "npx",
      "args": ["-y", "@railway/mcp-server"]
    }
  }
}

Connection setup:

1. Install Node.js 18+ on your system
2. Log in to Railway CLI: railway login (or set RAILWAY_TOKEN environment variable)
3. Add the configuration above to your .mcp.json file
4. Restart your MCP client to connect to Railway

Example tool usage:

# List project services
> Show me all services in the production project and their deployment status

# Deploy a service
> Trigger a new deployment for the api-server service from the main branch

# Check logs
> Show me the last 100 lines of logs from the worker service

Core Concepts

Concept	Purpose	Details
Projects	Application grouping	Top-level containers that hold related services, databases, and shared configuration
Services	Deployable units	Individual applications, APIs, or workers within a project that are built and deployed independently
Environments	Deployment stages	Isolated deployment targets (production, staging) with independent variables and configurations
Deployments	Release instances	Specific build-and-deploy events with unique IDs, logs, status, and rollback capabilities
Variables	Configuration management	Environment variables and secrets scoped to services and environments for runtime configuration

Architecture:

+------------------+       +------------------+       +------------------+
|  Railway         |       |  Railway MCP     |       |  AI Assistant    |
|  Platform API    |<----->|  Server (npx)    |<----->|  (Claude, etc.)  |
|  railway.app     | HTTPS |  stdio transport  | stdio |                  |
+------------------+       +------------------+       +------------------+
        |
        v
+--------------------------------------------------+
|  Projects > Services > Deployments > Logs > Vars  |
+--------------------------------------------------+

Configuration

Parameter	Type	Default	Description
RAILWAY_TOKEN	string	(from CLI login)	API token for authenticating with Railway's platform API
default_project	string	(auto-detect)	Default project ID to scope operations when not explicitly specified
default_environment	string	production	Default environment name for deployment and variable operations
log_lines	integer	200	Default number of log lines to retrieve when inspecting service output
auto_deploy	boolean	false	Whether to automatically trigger deployments when configuration changes are made

Best Practices

1. Use project-scoped tokens for MCP access. Generate Railway API tokens scoped to specific projects rather than using your account-level token. This limits the MCP server's access to only the projects you intend to manage through AI, reducing risk from token exposure.

2. Always specify the environment when managing variables. Railway supports multiple environments per service. When setting or reading environment variables, explicitly specify the target environment to avoid accidentally modifying production variables when you intended to change staging configuration.

3. Review deployment logs before triggering rollbacks. When a deployment fails, ask the AI to retrieve the build and runtime logs first. Understanding the failure cause ensures you roll back to the correct previous deployment and can address the root issue before redeploying.

4. Use Railway's built-in database services for development. Rather than managing external database connections, leverage Railway's integrated PostgreSQL, MySQL, and Redis services. The AI can help you provision these services and automatically configure connection variables for your application services.

5. Monitor deployment health through service metrics. After triggering a deployment, ask the AI to check the service health status and recent logs to confirm the new version is running correctly. This post-deployment verification catches issues early before users are affected.

Common Issues

"Unauthorized" error when connecting to Railway. Verify your Railway token is valid by running railway whoami in your terminal. If the token has expired, log in again with railway login or generate a new API token from your Railway dashboard under Account Settings > Tokens.

Deployment triggered but service shows "crashed" status. Check the build and runtime logs through the MCP server to identify the crash cause. Common issues include missing environment variables, port binding errors (Railway assigns the PORT variable automatically), and dependency installation failures.

Variables not reflecting after updates. Railway requires a new deployment for environment variable changes to take effect. After updating variables through the MCP server, trigger a redeploy of the affected service. Some changes to shared project-level variables may require redeploying all dependent services.