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Usage

This plugin is a client for a running Piston API. It does not install Piston runtimes for you, which keeps the Data Designer package portable across local, cluster, and remote service deployments.

Start a local sandbox

Use Docker on macOS or Linux:

bash plugins/data-designer-sandbox-piston/scripts/run-local-piston.sh

The script starts a container on http://localhost:2000. You can also use the Docker Compose example:

docker compose -f plugins/data-designer-sandbox-piston/docker/docker-compose.yml up --build

Piston runtime packages are stored under /piston inside a Docker volume. A new Piston package volume starts empty, so install the runtimes your workflow needs:

curl -X POST http://localhost:2000/api/v2/packages \
  -H 'Content-Type: application/json' \
  -d '{"language":"python","version":"3.12.0"}'

Execute a code column

from data_designer.config.config_builder import DataDesignerConfigBuilder

builder = DataDesignerConfigBuilder()
builder.add_column(
    name="result",
    column_type="code-sandbox",
    target_column="python_code",
    language="python",
    sandbox_url="http://localhost:2000",
)

Each output value is a JSON-serializable dictionary containing stdout, stderr, exit code, status, timing, and memory fields.

If a workflow needs Python packages, build or provide a custom Piston Python runtime first, then set version to that exact runtime version and list the expected packages in python_packages. The package list is metadata for humans and tool descriptions; execution still depends on the configured Piston runtime.

Add an MCP run_code tool

from data_designer_sandbox_piston import SandboxMCPConfig

sandbox_mcp = SandboxMCPConfig(
    name="sandbox",
    sandbox_url="http://localhost:2000",
    language="python",
    result_fields=["stdout", "stderr", "exit_code"],
)

mcp_provider = sandbox_mcp.to_provider()
tool_config = sandbox_mcp.to_tool_config()

Pass mcp_provider and tool_config into the Data Designer configuration path that configures MCP providers and tool aliases for your LLM columns.

If the sandbox URL is assigned by your launcher at runtime, omit sandbox_url from SandboxMCPConfig and set SANDBOX_URL in the environment inherited by the MCP subprocess.

Remote endpoint

For a remote deployment, use the same configuration with a different URL:

builder.add_column(
    name="result",
    column_type="code-sandbox",
    target_column="python_code",
    language="python",
    sandbox_url="https://piston.example.internal",
)

Keep the Piston endpoint private to trusted Data Designer workers or put it behind your own authentication proxy. The plugin forwards code to the configured endpoint and relies on that service for isolation and runtime availability.