Safely run Python snippets in a sandbox for analysis and quick validation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp_python_exec_server" yet — see the docs or source repo.
Please run the following Python code in a secure sandbox, verify whether this deduplication function works correctly, and show the output:
def unique_items(items):
return list(set(items))
print(unique_items([1,2,2,3,1]))Returns the execution result and explains whether the function behaves as expected.
Run Python in the sandbox to calculate the sum, average, and maximum of the following sales data, and present the results clearly: sales = [120, 150, 98, 210, 175]
Outputs statistical results such as total, average, and maximum.
Please execute a Python test snippet in the restricted environment to check whether this division function raises an error when the input is 0, and show the test result:
def divide(a, b):
return a / b
print(divide(10, 0))Returns the error or result to help identify boundary-condition issues.
Safely run Python code and manage packages for analysis and automation.
Safely run Python code with AI and MCP tool integration.
Run Python code securely with inline dependencies for fast experiments and analysis.
Run persistent Python sessions safely across multiple turns with sandboxing and timeouts.
A sandbox server for testing and debugging MCP tools and interactions.
Evaluate code in a sandbox with automated execution and LLM-based quality scoring.