marimo-sandbox

A FastMCP server that runs Python code inside auditable Marimo notebooks. Every execution is saved as a human-readable .py file you can open, inspect, and re-run at any time.

Why

When an AI agent (Claude Code, etc.) runs Python on your behalf, you get back stdout and maybe a traceback. You can't see the full code in context, can't re-run it, can't modify it interactively.

marimo-sandbox fixes this by wrapping every execution in a Marimo notebook:

• Auditable — the exact code that ran is saved as a .py file alongside its output
• Viewable — marimo edit <notebook> opens it in the browser with reactive cells
• Re-runnable — the notebook is standalone; python notebook.py works without the server
• Persistent — all runs stored in SQLite with stdout, stderr, status, code hash, and artifacts
• Safe — static risk analysis runs before every execution; critical patterns can require approval

Install

pip install marimo-sandbox
# or with uv:
uv pip install marimo-sandbox

Requires Python 3.11+ and marimo:

pip install marimo

Add to Claude Code

claude mcp add marimo-sandbox -- python -m marimo_sandbox

Or with uv:

claude mcp add marimo-sandbox -- uvx marimo-sandbox

Set a custom data directory (where notebooks and the database are stored):

claude mcp add marimo-sandbox \
  -e MARIMO_SANDBOX_DIR=/your/preferred/path \
  -- python -m marimo_sandbox

Tools (17)

run_python

Run Python code and get back results + a notebook you can open.

code              Python source to execute
description       Short label for this run (shown in list_runs)
timeout_seconds   Max execution time (default 60)
sandbox           Run in Docker with --network=none (default False)
packages          PyPI packages to install before running (e.g. ["pandas", "httpx"])
dry_run           If True, return static risk analysis only — do not execute (default False)
require_approval  If True, block execution when critical risk patterns are found (default False)

Returns: run_id, status, stdout, stderr, error, duration_ms, notebook_path, view_command, code_hash, artifacts, and optionally risk_findings, packages_installed, freeze.

Packages are installed via uv pip install when uv is available, falling back to pip. A full pip freeze snapshot is captured after installation and stored with the run.

Structured outputs

Your code can expose typed data to agents via the __outputs__ dict:

import pandas as pd
df = pd.read_csv("data.csv")
__outputs__["summary"] = df.describe().to_dict()
__outputs__["row_count"] = len(df)

Retrieve these values later with get_run_outputs.

Static risk analysis

Every call to run_python runs an AST-based risk scan before execution. Findings appear in risk_findings in the response. Use dry_run=True to get the analysis without running:

risk_findings severity tiers:
  critical  subprocess calls, os.system/popen, eval/exec/compile
  high      dangerous imports (os, subprocess, socket, requests, …)
  medium    open() with write/append mode
  low       os.environ[] access

Use require_approval=True to block execution when critical patterns are found. The response will include an approval_token — pass it to approve_run to proceed.

approve_run

Confirm a blocked run and execute it. Tokens expire after 1 hour.

approval_token   Token returned by run_python when status='awaiting_confirmation'
reason           Optional note explaining the approval

list_pending_approvals

List all runs currently awaiting approval, including expiry status and critical finding count.

list_artifacts

List files created by a run's code (everything in the notebook directory except the notebook itself and the result sidecar).

run_id   Run to inspect

Returns artifact_count and artifacts — each entry has path, size_bytes, extension.

read_artifact

Read the content of an artifact file. Path traversal is rejected. Large files are refused (default limit: 5 MB).

run_id          Run that created the file
artifact_path   Relative path from list_artifacts
max_size_bytes  Size limit in bytes (default 5 000 000)

Returns content (str) for text files or content_base64 for binary files, plus media_type, size_bytes, is_text.

get_run_outputs

Retrieve the structured __outputs__ dict written by the run. Returns {} if the run hasn't completed successfully or didn't populate __outputs__.

run_id   Run to read outputs from

rerun

Re-execute a previous run's code by run_id, optionally with modifications.

run_id            Run to re-execute
code              Override the code (default: use original)
description       Override the description (default: original + " (rerun)")
timeout_seconds   Max execution time (default 60)
sandbox           Run in Docker sandbox (default False)
packages          PyPI packages to install (default: reuse original run's packages)

open_notebook

Open a previous run in Marimo's interactive editor.

run_id   ID returned by run_python
port     Local port for the editor (default 2718)

Returns a url to open in your browser. You can then edit cells and re-run them.

cancel_run

Cancel a run that is currently executing (async_mode=True). Sends SIGTERM to the process and marks the run as cancelled in the database.

run_id   The run to cancel (must have status 'running')

Returns success, run_id, pid — or error if the run is not found or not running.

list_environments

List cached virtual environments (hash-based venv cache).

Each environment corresponds to a unique set of packages. Environments are reused automatically when run_python is called with the same package list.

Returns count and environments — each entry has env_hash, packages, size_bytes, created_at, last_used_at.

clean_environments

Delete cached virtual environments that haven't been used recently.

older_than_days   Delete envs whose last_used_at is older than this many days (default 90)

Returns deleted_count, deleted_hashes, freed_bytes.

diff_runs

Compare two runs and explain what changed between them. By default compares run_id against its parent run; supply compare_to to choose an explicit reference.

run_id      The run to inspect (the "after" run)
compare_to  ID of the reference run (the "before" run); defaults to run_id's parent

Returns run_a, run_b, relationship (parent_child / siblings / unrelated), summary flags, code_diff (including diff_text), env_diff, artifact_diff, output_diff, duration_diff, and a plain-English explanation.

list_runs

List recent runs with status, description, and timestamp.

limit    Max results (default 20)
status   Filter: 'success', 'error', or 'pending'
offset   Number of runs to skip for pagination (default 0)

Returns total, count, offset, and runs.

get_run

Full details of a specific run, including the four provenance fields stored per run: code_hash, env_hash, freeze, and risk_findings.

run_id                   Run to look up
include_code             Include submitted code (default True)
include_notebook_source  Include full .py notebook source (default False)

delete_run

Remove a run's database record and its notebook files from disk.

run_id         Run to delete
delete_files   Also remove the notebook directory (default True)

purge_runs

Bulk-delete runs older than N days to reclaim disk space.

older_than_days   Delete runs older than this many days (default 30)
delete_files      Also remove notebook directories (default True)
dry_run           Preview what would be deleted without deleting (default False)

When dry_run=False returns deleted_runs, files_deleted, run_ids. When dry_run=True returns dry_run=True, would_delete_runs, run_ids.

check_setup

Verify marimo, Docker, and uv are available and show the data directory.

Notebooks

Generated notebooks live at:

~/.marimo-sandbox/notebooks/{run_id}/notebook.py

Open any of them directly:

marimo edit ~/.marimo-sandbox/notebooks/run_a1b2c3d4/notebook.py

Or run headlessly:

python ~/.marimo-sandbox/notebooks/run_a1b2c3d4/notebook.py

A result sidecar is written alongside the notebook on success:

~/.marimo-sandbox/notebooks/{run_id}/{run_id}_result.json

Any other files your code writes to disk are captured as artifacts and accessible via list_artifacts / read_artifact.

Sandbox mode (Docker)

For untrusted code, run_python(sandbox=True) runs inside Docker with:

• --network=none — no outbound connections
• --memory=512m — memory cap
• --cpus=1 — CPU cap
• --read-only — read-only root filesystem
• writable /sandbox mount for the notebook and result file

Build the sandbox image first:

docker build -f Dockerfile.sandbox -t marimo-sandbox:latest .

Add packages your code needs to Dockerfile.sandbox and rebuild.

Configuration

Notebook structure

Every generated notebook has four fixed cells:

The __record__ → __execution__ dependency means: if your code raises an exception, __record__ never runs (Marimo's DAG won't execute a cell whose dependencies failed). The executor detects the missing sidecar and reports an error with the captured stderr.

Development

# Install in editable mode with dev dependencies
uv pip install -e ".[dev]"

# Lint
ruff check src/ tests/

# Type check
mypy src/

# Unit tests (fast, no subprocess)
pytest tests/ -m "not slow" -v

# Integration tests (run real Marimo subprocesses)
pytest tests/ -m slow -v

Known limitations

• Top-level return statements in submitted code are rejected (they exit the cell function before the sentinel is set). Wrap such code in a function.
• sys.exit() in user code is detected and reported as an error.
• Generated notebooks always import marimo. If marimo is not installed in the execution environment, the notebook will fail.