sandbox-execution

Isolated code execution runs in a sandbox to perform exploratory data analysis safely. You prepare a workspace, run custom python3 inline scripts, fetch artifacts, and teardown the environment for reproducible results.

Secure code prototyping follows a deterministic workflow: prepare the sandbox, run the job, check status, fetch artifacts, and teardown. This guided process ensures reproducible results without affecting production tools.

Yes, custom analysis scripts execute long-running analyses with a default network policy that denies external access. This secure execution environment provides safe resource controls and isolation for validating hypotheses.

You need a sandbox environment when existing domain MCP tools cannot perform the requested custom analysis. It applies to exploratory data science tasks, code prototyping, and validating hypotheses that require isolated execution.

Yes, the sandbox supports python3 for inline scripts to ensure reproducible workflows. It applies to exploratory data science tasks and secure execution of long-running analyses that require isolation.

Limitations include a default network policy that denies external access, restricting custom analyses from reaching external resources. The sandbox is designed for isolated, reproducible execution rather than network-dependent operations.