MCP server
by shibing624
Run Python code online securely with our Python program interpreter. Execute code, install packages, and manage files in
Executes Python code in a secure, isolated environment with persistent context and the ability to install packages and save files.
Python Code Interpreter is a community-built MCP server published by shibing624 that provides AI assistants with tools and capabilities via the Model Context Protocol. Run Python code online securely with our Python program interpreter. Execute code, install packages, and manage files in It is categorized under developer tools. This server exposes 4 tools that AI clients can invoke during conversations and coding sessions.
You can install Python Code Interpreter in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
Apache-2.0
Python Code Interpreter is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Strong directory entry: Python Code Interpreter surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Useful MCP listing: Python Code Interpreter is the kind of server we cite when onboarding engineers to host + tool permissions.
According to our notes, Python Code Interpreter benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
I recommend Python Code Interpreter for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Useful MCP listing: Python Code Interpreter is the kind of server we cite when onboarding engineers to host + tool permissions.
According to our notes, Python Code Interpreter benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Python Code Interpreter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Python Code Interpreter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Python Code Interpreter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Python Code Interpreter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
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Python interpreter, MCP server, no API key, free. Get results from running Python code.
This MCP server provides tools for running Python code, installing packages, and executing Python files. It can be easily integrated with MCP clients, including Claude and other LLM applications supporting the MCP protocol.
You can install the MCP Run Python Code Server using uv:
uv pip install mcp-run-python-code
Or using pip:
pip install mcp-run-python-code
git clone https://github.com/shibing624/mcp-run-python-code.git
cd mcp-run-python-code
pip install -e .
from run_python_code import RunPythonCode
tool = RunPythonCode(base_dir='/tmp/tmp_run_code/')
# 示例1:基本代码执行
result = tool.run_python_code("x = 10
y = 20
z = x * y
print(z)")
print(f"结果: {result}") # 输出: 结果: 200
# 示例2:保存并运行文件
result = tool.save_to_file_and_run(
file_name="calc.py",
code="a = 5
b = 15
c = a + b",
variable_to_return="c"
)
print(f"结果: {result}") # 输出: 结果: 20
# 实例3:安装python包
result = tool.pip_install_package("requests")
print(f"结果: {result}")

Run the server with the stdio transport:
uvx mcp-run-python-code
or
uv run mcp-run-python-code
or
python -m mcp-run-python-code
Then, you can use the server with any MCP client that supports stdio transport.
To add the weather MCP server to Cursor, add stdio MCP with command:
uvx mcp-run-python-code
You can also run the MCP server with FastAPI:
python run_python_code/fastapi_server.py
This will start a FastAPI server on http://localhost:8083 with the following endpoints:
GET /health - Check server healthPOST /execute - Execute Python codePOST /save-and-execute - Save Python code to a file and execute itPOST /install-package - Install a Python package using pipPOST /run-file - Run an existing Python fileGET /docs - Swagger API documentation
You can test the API using curl, detail in API Documentation.You can run the MCP server using Docker. First, build the Docker image:
docker build -t mcp-run-python-code .
Then, run the container:
docker run -p 8000:8000 -it mcp-run-python-code
also, you can use FastAPI server with Docker:
docker build -t fastapi-mcp-run-python-code -f Dockerfile.fastapi .
run the container:
sudo docker run -d --name mcp-python-service -p 8083:8083 --restart unless-stopped mcp-run-python-code
You can also use Docker Compose to run the MCP server along with other services. See Docker Usage for details.
run_python_code - Execute Python code and return print output or error messagesave_to_file_and_run - Save Python code to a file and execute itpip_install_package - Install Python packages using piprun_python_file - Run an existing Python file and optionally return a variable valuefrom run_python_code import RunPythonCode
tool = RunPythonCode(base_dir='/tmp/tmp_run_code/')
# Execute simple calculations
code = "result = 2 ** 10; print(f'Result: {result}')"
value = tool.run_python_code(code)
print(value) # Output: 1024
from run_python_code import RunPythonCode
tool = RunPythonCode(base_dir='/tmp/tmp_run_code/')
# Save code to a file and run it
script_code = """
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-2)
result = fibonacci(10)
print(f"Fibonacci(10) = {result}")
"""
result = tool.save_to_file_and_run("fib.py", script_code, "result")
print(result) # Output: 55
from run_python_code import RunPythonCode
tool = RunPythonCode(base_dir='/tmp/tmp_run_code/')
# JSON data processing
code = """
import json
data = {'name': '张三', 'age': 30}
json_str = json.dumps(data, ensure_ascii=False)
print(json_str)
"""
result = tool.run_python_code(code)
print(result) # Output: {"name": "张三", "age": 30}
This project is licensed under The Apache License 2.0 and can be used freely for commercial purposes.
Please include a link to the mcp-run-python-code project and the license in your product description.
We welcome contributions to improve this project! Before submitting a pull request, please:
tests directorypython -m pytest to ensure all tests passPrerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.