Railway MCP Server

The Railway MCP Server bridges the gap between AI assistants and the Railway.app platform, giving natural‑language agents a fully‑featured interface to manage infrastructure. With this server, Claude or any MCP‑compatible client can authenticate using a Railway API token and then perform routine operations—listing projects, creating or restarting deployments, adding environment variables, or even spinning up new services from a GitHub repo—all without leaving the chat. This eliminates the need for manual CLI commands or browser interactions, streamlining DevOps workflows and allowing developers to focus on code rather than tooling.

At its core, the server offers a rich set of capabilities that map directly to Railway’s REST API. Authentication is handled securely via bearer tokens, and once authenticated developers can query the current state of their Railway account: list projects, fetch detailed project information, and delete obsolete ones. Deployment management lets agents restart or view the status of existing deployments, while service creation tools support both Docker images and GitHub‑based templates. Variable management is fully supported, enabling the creation, update, or deletion of environment variables on the fly. Additional features include network configuration for services and volume management for persistent storage, giving agents control over the full lifecycle of a Railway deployment.

The value proposition is clear for teams that rely on AI assistants to accelerate infrastructure provisioning. For example, a product manager can ask Claude to “deploy the latest version of our API service from GitHub” and receive a confirmation once the deployment is live. A QA engineer might request “list all variables for project X” to verify that the staging environment is correctly configured. Because the MCP server exposes these actions as conversational prompts, developers can iterate quickly without context switching between IDEs and dashboards.

Real‑world scenarios include continuous integration pipelines where an AI assistant triggers a new Railway deployment after a successful build, or incident response workflows that involve spinning up a debugging instance with specific environment variables. The server’s integration with popular MCP clients—Claude Desktop, Cursor, and others—means it can be dropped into existing workflows with minimal friction. The design also anticipates future expansion: as Railway adds more templates and automated networking features, the MCP server will expose them through additional tools, keeping AI agents up to date with platform capabilities.

In summary, the Railway MCP Server turns Railway.app into a conversational API. It empowers developers to harness AI assistants for end‑to‑end infrastructure management, reducing friction, accelerating delivery cycles, and ensuring that the full breadth of Railway’s features is accessible through natural language commands.