Python Exec Sandbox vs Container Use
A side-by-side look at the Python Exec Sandbox and Container Use MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Python Exec Sandbox for code execution work as a local process; choose Container Use for code execution. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Python Exec Sandbox when
- You need dedicated capabilities in the Code Execution domain.
- You prefer a local stdio subprocess transport.
- You want tools like
execute_python, check_environment, validate_script.
Choose Container Use when
- You need dedicated capabilities in the Code Execution domain.
- Its auth model fits you: No auth required.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Code Execution | Code Execution |
| Quality signal | 52/100 · Good | 51/100 · Good |
| Install command | Confirmed | Not confirmed yet |
| Transport | Local subprocess (stdio) | — |
| Auth | Not declared | No auth required |
| Pricing | Not declared | Free |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1 | 4,078 |
| Directory activity | 2 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 3 | Not listed |
| Full listing | View Python Exec Sandbox | View Container Use |
Tools & capabilities
Python Exec Sandbox tools 3
execute_pythonExecute a Python script with automatic dependency management. The script can include PEP 723 inline metadata (# /// script blocks) for declaring dependencies. Additional dependencies can also be passed via the dependencies parameter and will be merged. Args: script: Python source code to execute. May include PEP 723 metadata. dependencies: Extra PEP 508 dependency specifiers to make available. timeout_seconds: Maximum execution time (1-300, default 30). Returns: Formatted output with stdout, stderr, exit code, and duration. Example - simple script: execute_python(script="print('hello')") Example - with dependencies parameter: execute_python( script="import requests; print(requests.get('https://example.com').status_code)", dependencies=["requests>=2.32"] ) Example - with inline dependency metadata (preferred for multiple deps): execute_python(script=''' # /// script # dependencies = ["pandas>=2.2", "numpy>=1.26"] # /// import pandas as pd import numpy as np print(pd.DataFrame({"a": np.arange(5)}).describe()) ''') Always pin dependency versions (e.g. "pandas>=2.2" instead of "pandas") for reproducible results. The inline metadata block (# /// script ... # ///) is the recommended way to declare dependencies directly in the script (see PEP 723: https://peps.python.org/pep-0723/). The dependencies parameter is a simpler alternative when you just need to add a few packages. Both accept standard pip-style version specifiers like "requests>=2.28" or "pandas" (see PEP 508: https://peps.python.org/pep-0508/).check_environmentCheck the execution environment and report status. Returns information about Python version, uv version, platform, sandbox configuration, and cache status.validate_scriptValidate a Python script's PEP 723 metadata and dependencies without executing it. Checks metadata syntax, dependency format, and requires-python compatibility. Args: script: Python source code to validate. May include inline dependency metadata (# /// script blocks, see https://peps.python.org/pep-0723/). dependencies: Extra dependency specifiers to validate, using standard pip-style format like "requests>=2.28" (see https://peps.python.org/pep-0508/). Returns: Validation result with metadata details or error information.
Container Use tools
No tool names declared yet. Check the project README from the listing page.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Python Exec Sandbox
{
"mcpServers": {
"python-exec-sandbox": {
"command": "uvx",
"args": [
"mcp-python-exec-sandbox"
]
}
}
}
Container Use
No confirmed install command for Container Use yet — follow the setup steps linked from its listing page rather than a guessed package name.
Frequently asked questions
Python Exec Sandbox is categorized under Code Execution and uses a local stdio subprocess. Container Use belongs to Code Execution and uses an install path we have not confirmed yet. Pick Python Exec Sandbox for code execution work and Container Use for code execution.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client. Container Use has no confirmed install command yet — follow its own setup instructions.
Python Exec Sandbox has no declared pricing — check the project's own site. Container Use is listed as free. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add python-exec-sandbox and dagger-container-use as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Python Exec Sandbox has a quality signal of 52/100 (Good) with 2 views, 0 installs and 1 GitHub stars. Container Use has 51/100 (Good) with 3 views, 0 installs and 4,078 GitHub stars.