Railway MCP Server

A Model Context Protocol server that lets you manage Railway infrastructure from claude.ai (or any MCP client). Built with FastMCP and deployed on Railway itself.

What It Does

17 tools across 5 categories:

• Projects - List and inspect your Railway projects

• Services - View service configuration and status per environment

• Environments - List, create, and duplicate environments

• Variables - List, set, bulk set, and delete environment variables

• Deployments - Check status, read logs, redeploy, and restart

Why

Railway has an official MCP server but it wraps the CLI and only works locally (stdio transport). This server hits the Railway GraphQL API directly over HTTP, so it can be deployed as a remote service and connected to claude.ai as a connector.

Setup

1. Get a Railway API Token

Go to railway.com/account/tokens and create an account-level token.

2. Deploy to Railway

• Create a new Railway project

• Connect this GitHub repo

• Set environment variables:

  • RAILWAY_API_TOKEN = your token

  • PORT = 8000

  • MCP_TRANSPORT = streamable-http

• Generate a public domain

3. Connect to claude.ai

Add the MCP connector URL: https://{your-domain}/mcp

Local Development

# Clone and install
git clone https://github.com/Travis-Gilbert/railway-mcp.git
cd railway-mcp
pip install -e .

# Set up env
cp .env.example .env
# Edit .env with your Railway API token

# Run locally
python -m railway_mcp

# Or with stdio transport
MCP_TRANSPORT=stdio python -m railway_mcp

Tech Stack

• Python 3.12

• FastMCP (MCP protocol + transport)

• httpx (async HTTP client)

• Pydantic v2 (input validation)

• Railway GraphQL API v2