Python Exec Sandbox vs Figma Context MCP
A side-by-side look at the Python Exec Sandbox and Figma Context MCP 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 Figma Context MCP for architecture & design as a local process. 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 Figma Context MCP when
- You need dedicated capabilities in the Architecture & Design domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
FIGMA_API_KEY, PORT. - You want tools like
get_figma_data, download_figma_images.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Code Execution | Architecture & Design |
| Quality signal | 52/100 · Good | 71/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | API key required |
| Pricing | Not declared | Free |
| Required env vars | None listed | FIGMA_API_KEYPORT |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1 | 16,047 |
| Directory activity | 2 views 0 installs 0 upvotes | 9 views 0 installs 0 upvotes |
| Tools | 3 | 2 |
| Full listing | View Python Exec Sandbox | View Figma Context MCP |
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.
Figma Context MCP tools 2
get_figma_dataGet comprehensive Figma file data including layout, content, visuals, and component informationdownload_figma_imagesDownload SVG and PNG images used in a Figma file based on the IDs of image or icon nodes. Images will be saved relative to the server's image directory: /home/user
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"
]
}
}
}
Figma Context MCP
{
"mcpServers": {
"glips-figma-context-mcp": {
"command": "npx",
"args": [
"-y",
"figma-developer-mcp"
],
"env": {
"FIGMA_API_KEY": "YOUR_FIGMA_API_KEY_HERE",
"PORT": "YOUR_PORT_HERE"
}
}
}
}
Frequently asked questions
Python Exec Sandbox is categorized under Code Execution and uses a local stdio subprocess. Figma Context MCP belongs to Architecture & Design and uses a local stdio subprocess. Pick Python Exec Sandbox for code execution work and Figma Context MCP for architecture & design.
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.
Python Exec Sandbox has no declared pricing — check the project's own site. Figma Context MCP is listed as free. Auth: api key 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 glips-figma-context-mcp 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. Figma Context MCP has 71/100 (Great) with 9 views, 0 installs and 16,047 GitHub stars.