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Create sandboxes, run commands, and manage files on Railway.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "railway-sandbox-mcp" yet — see the docs or source repo.
Create a new ephemeral isolated Linux sandbox on Railway, clone my project, run npm install and npm test, and return a summary of the command output.
A summary of command execution results in the new sandbox, including install and test output.
In an existing Railway sandbox, create a reports directory, write a notes.txt file, and then list the files in that directory.
Confirmation of file operations and a list of files in the directory.
Run a set of setup commands in a Railway sandbox, create a checkpoint, and then continue with follow-up commands from that checkpoint.
Confirmation that the checkpoint was created and the results of follow-up commands from that state.
Developers can run install, build, or debugging commands in an ephemeral Linux sandbox without affecting their local environment. It fits cases where command results need to be verified quickly.
Users can create, modify, and inspect files inside a Railway sandbox for temporary outputs or experimental working directories. It is useful when they do not want to keep intermediate files locally.
When environment setup involves multiple steps, users can create a checkpoint and continue later from that state. This helps reduce repeated initialization work.
It enables ChatGPT to create environments, run commands, perform file operations, and use checkpoints in ephemeral isolated Linux sandboxes on Railway. It is suited for development or DevOps tasks that need isolated execution.
Based on the description, it runs in ephemeral isolated Linux sandboxes provided on Railway. More installation or configuration details are not provided; see the source repository.
It emphasizes running commands in an ephemeral, isolated Linux sandbox rather than directly in a local environment. It also supports file operations and checkpointing.
Give AI agents authenticated sandbox access and a restricted path for safe production deploys.
Manage Railway projects, deployments, variables, and domains directly in AI chat.
Provide AI agents a local isolated Linux sandbox for fast, safe commands.
Run commands, manage long jobs, and transfer files in AI sandboxes.
Securely run Python code in a sandbox and return execution results.
Safely run any code in isolated Docker containers for testing and automation.