SandboxRunner

SandboxRunner is a custom Model Context Protocol (MCP) server that enables AI assistants and MCP-compatible clients to securely execute Python and C++ code snippets inside disposable, isolated Docker containers — and receive back structured results (stdout, stderr, exit code, and timing) in real time.

Built to understand MCP server design, Docker-based process isolation, and security-boundary thinking from the ground up.

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Table of Contents

• Features
• Architecture
• Requirements
• Installation
• Configuration
• MCP Tools
• Isolation & Resource Limits
• Execution History
• Usage
• Project Structure
• Testing
• Known Limitations
• Contributing

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Features

• ✅ Python execution — runs snippets inside python:3.12-slim
• ✅ C++ execution — two-stage compile (g++ -std=c++17 -O2) + run, using gcc:14
• ✅ Strong isolation per run:
  • No network access (--network none)
  • Hard memory cap (default 256m)
  • Hard CPU cap (default 0.5 cores)
  • Read-only root filesystem with a temporary scratch-only mount
  • noexec tmpfs for /tmp
  • Containers are ephemeral — deleted after every run
• ✅ Independent timeout enforcement — a hung snippet cannot hang the MCP server; the container is force-killed on timeout
• ✅ Output size capping — stdout/stderr truncated at 100 KB with a clear marker
• ✅ Distinct compile vs. runtime errors for C++ — compiler errors are surfaced separately from runtime crashes
• ✅ Local execution history — every run is logged to a local SQLite database and queryable via MCP
• ✅ Actionable Docker errors — clear error message if Docker is not running, instead of a silent hang
• ✅ All tunables in one place — config.py is the single source of truth for limits, images, and settings

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Architecture

MCP Client ──(stdio / sse)──▶  SandboxRunner (FastMCP)
                                       │
                                       ▼
                             execution.py (orchestration)
                                       │
                         ┌─────────────┴─────────────┐
                         ▼                           ▼
              Docker (Python)              Docker (C++)
           python:3.12-slim             gcc:14 container
           --network none               --network none
           --memory 256m                --memory 256m
           --cpus 0.5                   --cpus 0.5
           --read-only                  Stage 1: compile (rw /scratch)
                                        Stage 2: run     (ro /scratch)
                                       │
                                       ▼
                          database.py → sandbox_history.db (SQLite)

Per-execution flow (run_code)

1. Validates input — language, code size, timeout ceiling
2. Writes code to a temporary host directory
3. Mounts that directory into a fresh Docker container as /scratch
4. Python: runs python /scratch/snippet.py directly
5. C++: compiles to /scratch/a.out (read-write mount), then runs the binary (read-only mount) in a second container — compile errors are returned distinctly from runtime errors
6. Enforces timeout at the server level; kills the container if exceeded
7. Captures and truncates stdout/stderr
8. Logs run metadata to SQLite
9. Returns a structured result to the MCP client

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Requirements

• Python ≥ 3.12
• uv — package manager (pip install uv or see uv docs)
• Docker Desktop or Docker Engine — must be installed and running
• Docker images (pre-pull recommended):
  • python:3.12-slim
  • gcc:14

Python Dependencies

Package	Purpose
mcp[cli] >= 1.28.1	Official MCP SDK (FastMCP server + CLI)
docker >= 7.1.0	Docker SDK — container orchestration

Dev only:

Package	Purpose
pytest >= 9.1.1	Test runner
pytest-asyncio >= 1.4.0	Async test support

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Installation

# 1. Clone the repository
git clone https://github.com/your-username/sandbox-runner.git
cd sandbox-runner

# 2. Ensure Docker is running
docker ps

# 3. Install dependencies and create the virtual environment
uv sync

# 4. Pre-pull the Docker images (avoids slow cold start on first run)
docker pull python:3.12-slim
docker pull gcc:14

The sandbox-runner console script is installed automatically via [project.scripts] in pyproject.toml.

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Configuration

All tunables live in src/sandbox_runner/config.py. No environment variables are required beyond the optional transport flag.

Setting	Default	Description
DEFAULT_TIMEOUT_SECONDS	10	Default execution timeout
MAX_TIMEOUT_SECONDS	30	Hard ceiling — cannot be exceeded by callers
MEMORY_LIMIT	"256m"	Per-container memory cap
CPU_LIMIT	"0.5"	Per-container CPU share (in cores)
MAX_CODE_SIZE_BYTES	51200 (50 KB)	Maximum allowed snippet size
MAX_OUTPUT_SIZE_BYTES	102400 (100 KB)	Output truncation threshold
DB_FILE	"sandbox_history.db"	SQLite file for execution history
DEFAULT_HISTORY_LIMIT	20	Default rows returned by history tool

To add a new language, add an entry to SUPPORTED_LANGUAGES in config.py with an image, run_cmd, and optionally compile_cmd / source_file for compiled languages.

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MCP Tools

Tool	Description	Inputs	Outputs
run_code	Execute a code snippet in an isolated container	language (python|cpp), code, timeout_seconds (opt, default 10, max 30)	status, exit_code, stdout, stderr, duration_ms, language
list_supported_languages	List available languages and Docker images	—	[{language, image, description}]
get_execution_history	Retrieve recent run records	limit (opt, default 20)	[{id, timestamp, language, code_snippet, status, exit_code, duration_ms, stdout_size, stderr_size}]

status values: success · error · timeout · compile_error (C++ only)

Example run_code response:

{
  "status": "success",
  "exit_code": 0,
  "stdout": "The sum of elements is: 15\n",
  "stderr": "",
  "duration_ms": 1823.47,
  "language": "cpp"
}

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Isolation & Resource Limits

Each run is sandboxed with the following Docker constraints:

Constraint	Value	Effect
--network none	Enforced	No outbound or inbound network access
--memory 256m	Configurable	Hard memory ceiling per container
--cpus 0.5	Configurable	CPU share cap
--read-only	Always on	Root filesystem is immutable
tmpfs /tmp	size=64m,noexec	Small, non-executable temp space
--rm	Always on	Container is deleted after each run
Timeout kill	Server-level	Server kills container if it exceeds timeout

⚠️ This uses Docker-level isolation — suitable for personal/local use. It is not a hardened multi-tenant sandbox (e.g. gVisor, Firecracker) and is not intended for running untrusted third-party code.

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Execution History

Every run is stored in a local SQLite database (sandbox_history.db):

CREATE TABLE IF NOT EXISTS runs (
    id           INTEGER PRIMARY KEY AUTOINCREMENT,
    timestamp    REAL    NOT NULL,
    language     TEXT    NOT NULL,
    code_snippet TEXT    NOT NULL,   -- first 500 chars only
    status       TEXT    NOT NULL,
    exit_code    INTEGER,
    duration_ms  REAL    NOT NULL,
    stdout_size  INTEGER NOT NULL DEFAULT 0,
    stderr_size  INTEGER NOT NULL DEFAULT 0
);

• Only the first 500 characters of each snippet are persisted as a preview.
• stdout_size / stderr_size store byte counts, not the full content.
• Timestamps are returned as ISO 8601 UTC strings via the get_execution_history tool.

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Usage

Registering with an MCP Client

Add SandboxRunner to your MCP client's configuration. Example for Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "sandbox-runner": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/sandbox-runner",
        "run",
        "sandbox-runner"
      ]
    }
  }
}

Replace /absolute/path/to/sandbox-runner with your actual cloned directory path.
On Windows: C:\\Users\\YourName\\path\\to\\sandbox-runner

Restart the MCP client after saving the config. The three tools will become available immediately.

Running Standalone

# Start with stdio transport (default — for MCP clients like Claude Desktop)
uv run sandbox-runner

# Start with SSE transport (for web-based or HTTP MCP clients)
uv run sandbox-runner --transport sse

# View CLI help
uv run sandbox-runner --help

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Project Structure

sandbox-runner/
├── src/
│   └── sandbox_runner/
│       ├── __init__.py       # Package version
│       ├── config.py         # All tunables — limits, images, DB path
│       ├── database.py       # SQLite connection, record_run, fetch_history
│       ├── execution.py      # Docker orchestration, validation, truncation
│       ├── main.py           # CLI entrypoint (argparse + transport)
│       └── server.py         # FastMCP server + tool definitions
├── tests/
│   ├── test_execution.py     # Input validation + mocked Docker execution tests
│   └── test_server.py        # Mocked MCP tool-level tests
├── pyproject.toml            # Project metadata, deps, build config
├── uv.lock                   # Locked dependency tree
├── .python-version           # Pinned Python version
├── sandbox_history.db        # SQLite history (auto-created at runtime)
└── README.md

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Testing

The test suite uses mocked Docker calls — Docker does not need to be running to run the tests.

uv run pytest

Current coverage:

• test_execution.py

  • Valid input passes without errors
  • Unsupported language raises ValueError
  • Empty code raises ValueError
  • Oversized code raises ValueError
  • Timeout exceeding max raises ValueError
  • Mocked end-to-end Python execution — asserts status == "success" and correct stdout

• test_server.py

  • run_code success path — verifies result format and DB logging call
  • run_code invalid language — verifies error response and that DB is not written
  • list_supported_languages — returns entries for both python and cpp
  • get_execution_history — timestamps formatted as ISO 8601 strings

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Known Limitations

• Docker must be running. The server returns a clear error if the Docker daemon is unreachable — no silent hangs.
• Docker-level isolation only. Not a hardened cloud-grade sandbox. Designed for single-user, local use.
• No multi-tenancy. No auth, no rate limiting — assumes a single trusted operator.
• Fixed resource limits by default. 256 MB / 0.5 CPU / 10s default timeout may be restrictive for heavy compute workloads.
• History stores only snippet previews. Full code content is not persisted; only the first 500 characters are stored.

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Contributing

Contributions are welcome!

1. Fork the repo and create a feature branch
2. Run uv run pytest and ensure all tests pass
3. Keep new tunables in config.py — avoid hardcoding values elsewhere
4. Open a Pull Request with a clear description of the change and its motivation

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