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Run commands, manage long jobs, and transfer files in AI sandboxes.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "sandbox-mcp" yet — see the docs or source repo.
Run the following commands in an isolated sandbox: git status && pytest -q. Return the full output, an error summary, and suggested next fixes.
Command results, a summary of test failures, and actionable fix recommendations.
Start a background training job in the Docker sandbox: python train.py --epochs 50. Record the job ID, provide periodic log summaries, and return the output file list when finished.
A job ID, periodic status updates, log summaries, and a final list of generated artifacts.
Transfer a local data archive into the sandbox, extract it, count rows and schema for each CSV file, and return the results in a table.
File transfer and processing status, plus a structured table of row counts and column schemas for each CSV.
Provide AI agents a local isolated Linux sandbox for fast, safe commands.
Safely read, write, search, and manage files in a sandboxed environment.
Run AI agents in VM-isolated sandboxes on Mac for safer execution.
Connect AI to remote servers via SSH for commands and secure file transfer.
Safely run Python code, capture output, and generate charts.
Build, debug, and manage software tasks with natural language across LLMs.