Railway MCP. Manage projects, deployments, and services from chat.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Railway MCP Server gives your AI agent direct control over your cloud infrastructure. Manage projects, deployments, services, and environment variables—all from your terminal or IDE.
You can create new projects with `create_project`, check build status using `list_deployments`, or restart a container instantly with `restart_service`. It's programmatic ops for modern DevOps.
What your AI agents can do
Create project
Builds a new Railway project container in your account.
Delete project
Deletes an entire Railway project; this action cannot be undone.
Get project
Retrieves detailed information for a specific Railway project ID.
The agent can create new projects (create_project), retrieve details on existing ones (get_project), or permanently delete a project container.
You can list all deployments for a specific service, check the status of production builds, and manually initiate a new deployment cycle with trigger_deploy.
The agent lists active service instances (get_service_instances) and can force-restart any running container to fix connectivity issues.
Quickly check or update sensitive configuration keys across your services using list_variables.
The agent can list all projects accessible by your token (list_projects) and pull basic user profile data with whoami.
Get specific architectural details for a single Railway project ID using the get_project tool.
Ask AI about this MCP
Supported MCP Clients
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Railway MCP Server: 10 Tools for DevOps Ops
Use these tools to programmatically control your entire Railway infrastructure—from creating new projects to restarting mission-critical services.
019d75fccreate project
Builds a new Railway project container in your account.
019d75fcdelete project
Deletes an entire Railway project; this action cannot be undone.
019d75fcget project
Retrieves detailed information for a specific Railway project ID.
019d75fcget service instances
Fetches the runtime configuration details for a running service instance.
019d75fclist deployments
Shows all historical and current deployments for a specific project, environment, or service.
019d75fclist projects
Lists every Railway project you have access to via your API token.
019d75fclist variables
Shows all stored environment variables for a given service container.
019d75fcrestart service
Force-restarts a running service instance, useful for clearing temporary state issues.
019d75fctrigger deploy
Starts a new deployment cycle for an existing service container.
019d75fcwhoami
Retrieves the profile information of the authenticated Railway user.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Railway, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Railway MCP Server: Cloud Ops for Your AI Agent
You're connecting your cloud infrastructure to an AI agent, giving it direct control over your deployments and services from the terminal. Forget clicking through dashboards—you can manage projects, check build statuses, and restart containers programmatically. This is operational DevOps, built right into your chat client.
Project Lifecycle Management
You'll start by seeing everything you have access to. Use list_projects to pull a full list of every Railway project tied to your API token. If you need a new sandbox environment, just ask the agent to run create_project, and it'll build out that container for you instantly. Need details on an existing setup? Run get_project with a specific Project ID; it pulls all the architectural info you need.
When you’re done with a project and want to wipe the slate clean, use delete_project. Just remember, this action is irreversible.
Deployment Monitoring and Control
Keeping your services updated is where things get serious. To see if a build went sideways or just finished up, run list_deployments. You can scope this check to an entire project, a specific environment (like staging or production), or even one single service container—it gives you the full history and status of every rollout.
If everything looks good and you need fresh code live, trigger a new deployment cycle using trigger_deploy. It kicks off that build immediately. You can also check the current state of your running services by calling get_service_instances to pull all the runtime configuration details.
Service Maintenance and Configuration
If a service gets flaky, you don't want to manually mess with it. The agent handles that: use restart_service to force-restart any container instance. This is perfect for clearing out temporary state issues or connectivity glitches without downtime anxiety. When it comes to secrets and settings, you can't afford mistakes.
Use list_variables to pull all the stored environment variables for a given service container. You'll need this list whenever you gotta verify sensitive config keys across your whole setup.
Auditing Your Infrastructure
Need to know who you are or what projects you have? Run whoami to retrieve the profile information of the user authenticated with Railway. This gives you basic assurance that everything's pointing to the right account. The combination of these tools lets your agent manage every layer—from creating a brand-new project container with create_project and listing all available ones via list_projects, to getting deep architectural data on one specific setup using get_project.
You're talking about total, programmatic control over the whole stack.
How Railway MCP Works
- 1 Enable the MCP server integration and provide your Railway API Token to the configuration.
- 2 Tell your AI agent what you need—e.g., 'List all my active projects' or 'Restart the auth service'.
- 3 The agent runs the appropriate tool (like
list_projectsorrestart_service) and gives you the real-time output directly in chat.
The bottom line is that your AI client talks to Railway directly, treating your entire cloud setup like a set of functions it can call on demand.
Who Is Railway MCP For?
This is for the Ops Engineer who spends too much time clicking through dashboards just to check if a service container restarted correctly. It's for developers tired of having to copy-paste IDs and API endpoints into separate CLI windows—you need one place to manage everything.
Uses list_projects to quickly scope out the entire cloud architecture before a major change, or uses restart_service when an unhealthy container pops up at 2 AM.
Triggers deployments (trigger_deploy) immediately after merging a pull request and checks the rollout logs via list_deployments without leaving their IDE.
Runs quick connectivity checks or verifies environment variables using list_variables across multiple services to confirm scaling integrity.
What Changes When You Connect
- Saves time by letting you list all active projects (
list_projects) without navigating the web dashboard. You get the full project inventory instantly, right in your agent's response. - You can check build health and trigger new releases directly. Use
trigger_deployto push a change, then runlist_deploymentsimmediately after to confirm success or spot errors. - Fixing flaky services is faster than logging into the dashboard. Running
restart_serviceforces a container cycle and gets you back online fast—no manual steps needed. - Managing secrets used to be painful. Use
list_variablesto pull all necessary environment keys for debugging or configuration checks, without opening multiple tabs. - Need to scope out the architecture? A single call to
get_projectgives you a deep dive into one service's setup—all the details you need in plain text.
Real-World Use Cases
The container is flaky after an update.
A user notices their 'Auth Service' keeps dropping connections. Instead of logging into Railway, they ask the agent to run get_service_instances first, confirming the current state. Then, they tell it to execute restart_service. The agent handles both steps and confirms the container is recycling successfully.
Need to audit all active microservices.
The team needs a full inventory of what's running. They simply ask for an overview, and the agent uses list_projects to dump every single project name and ID into the chat window. No clicking through paginated results required.
A feature requires new secret keys.
Before deploying a critical backend component, an engineer needs to check if all environment variables are set up correctly for staging. They run list_variables on the target service, verifying that sensitive API keys and endpoints are present before giving the green light.
The latest production build failed.
Instead of waiting until someone manually notices a failing deployment, the engineer prompts the agent to check the status using list_deployments. The agent immediately reports the last successful state and alerts them if the current build is stuck or marked 'FAILED'.
The Tradeoffs
Using a standard CI/CD dashboard
You have to jump between the project overview, then click into the service tab, then find the deployment logs, and finally run a separate command just to restart it. It's slow.
→ Just ask your AI agent to handle the entire sequence: 'Check deployments for ECommerce Backend, and if the status is old, please trigger_deploy and restart_service.' The agent runs the tools in order.
Manual API calls/Scripts
Writing a Python script just to list all projects (list_projects) or check variable names requires you to manage authentication tokens and complex error handling within your own code.
→
The MCP Server handles the token management and execution flow. You just talk to the agent, which executes list_variables or get_project using the secure connection.
Relying on UI search filters
Trying to find a specific service instance's runtime configuration requires navigating multiple dropdown menus and filtering results—a waste of time.
→
Use get_service_instances. Give the agent the project name, and it pulls the exact configuration data directly without any UI interaction.
When It Fits, When It Doesn't
Use this MCP Server if your primary need is real-time operational control over an existing cloud deployment. You want to run commands—like restart_service or trigger_deploy—and see the results immediately, all within a chat context. It’s perfect for debugging and maintenance tasks.
Don't use this if you are building a complex data pipeline that needs to ingest data from many external sources (use a dedicated workflow engine). Also, don't rely on it as your primary source of truth; always cross-reference with the Railway UI. Use list_projects to see what exists, but treat the agent’s output as an instruction set for action, not definitive documentation.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Railway. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Debugging cloud services shouldn't require a dozen browser tabs open.
Right now, if your service is flaky, you have to do this: Open the Railway dashboard. Find the project ID. Click on the service. Check the deployment history for failures. Copy the environment variable keys needed for debugging. Then, finally, open a separate terminal and run a `docker restart` command manually.
With this MCP Server, your AI agent handles that whole sequence in one go. You just ask it to fix the service; it runs `get_service_instances`, checks logs, and executes `restart_service`. It gives you the actionable result without the click fatigue.
The Railway MCP Server: Instant project visibility with list_projects.
Today, finding out what projects your team actually has running means logging into the dashboard and clicking through multiple pages of listed items. You often miss side projects or forgotten staging environments because they're buried deep in subfolders.
Now, just prompt for a list. The agent runs `list_projects` and gives you every single project name and ID right there. It’s clean, immediate inventory control.
Common Questions About Railway MCP
How do I check if my latest deployment succeeded using list_deployments? +
Use list_deployments. This tool lists all historical and current builds for your service. You can filter the results to confirm if the target environment shows a 'SUCCESS' state.
Can I reset my running container with restart_service? +
Yes, restart_service forces a recycling of the service instance. This is useful when you know the service is stuck in an unstable or memory-leaking state and needs to come back online.
What should I use if I need to see all my projects? (list_projects) +
Just call list_projects. It pulls a clean list of every project accessible with your token, giving you an immediate overview of the entire cloud architecture.
Where do I manage environment variables using list_variables? +
The agent uses list_variables to pull all configured keys for a specific service. This is the safest way to check or update secrets without manual UI navigation.
How do I confirm my current user identity using whoami? +
Use whoami to retrieve your authenticated Railway profile details. This confirms which specific user context your AI client is operating under before it makes any changes or reads sensitive data.
What detailed information does get_project provide about a single project? +
Running get_project returns deep metadata for one specified project ID. You'll find things like its creation date, associated services list, and overall architecture map without needing to open the web dashboard.
How do I manually force a new deployment cycle using trigger_deploy? +
trigger_deploy starts a fresh build immediately for a service. Use this when you need an urgent test run or want to bypass scheduled pipelines and ensure the latest code gets deployed right away.
What specific runtime data can I get_service_instances show me? +
This tool provides the current operational configuration for a service. It lists details like memory usage, active ports, and network connectivity status—which is crucial information when debugging performance issues.
How do I create a Railway API token? +
Log into your main Railway dashboard. Navigate to your Account Settings and head to the 'Tokens' section. Generate a standard Personal Access API Token, copy it definitively, and paste it into the secure configuration field provided by the MCP integration here.
Can I target specific environments (like Staging vs Production)? +
Yes. Each API operation supports passing explicit project_id and environment_id variables, ensuring the AI performs operations strictly inside your requested boundaries.
Is restarting a service destructive? +
It behaves identically to clicking 'Restart' on the Railway dashboard. It forces an immediate container recycle for the specific service based on the last successful build.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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