Python Code Interpreter▌
by shibing624
Run Python code online securely with our Python program interpreter. Execute code, install packages, and manage files in
Provides a secure Python code interpreter for executing code, installing packages, and running files in isolated environments with persistent execution contexts and variable extraction capabilities.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Data analysis and prototyping
- / Teaching and learning Python concepts
- / Testing code snippets before deployment
- / Quick calculations and script automation
capabilities
- / Execute Python code snippets
- / Install Python packages via pip
- / Save code to files and run them
- / Extract specific variable values from executed code
- / Run existing Python files
what it does
Executes Python code in a secure, isolated environment with persistent context and the ability to install packages and save files.
about
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.
how to install
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.
license
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.
readme
mcp-run-python-code
Python interpreter, MCP server, no API key, free. Get results from running Python code.
Overview
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.
Features
- Execute Python code in a safe environment
- Install Python packages using pip
- Save Python code to files and run them
- Run existing Python files
- Return specific variable values from executed code
- Error handling and debugging support
Installation
From pip
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
From source
git clone https://github.com/shibing624/mcp-run-python-code.git
cd mcp-run-python-code
pip install -e .
Usage
Python Demo
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}")

Running as a standalone MCP server
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.
Integrating with Cursor
To add the weather MCP server to Cursor, add stdio MCP with command:
uvx mcp-run-python-code
Running the FastAPI server
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.
Run with Docker
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.
Tools available
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 value
Examples
Example 1: Basic Code Execution
from 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
Example 2: Run python File
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
Example 3: Data Processing
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}
Contact
- Issues and suggestions:
- Email: [email protected]
- WeChat: Add me (WeChat ID: xuming624) with the message: "Name-Company-NLP" to join our NLP discussion group.
License
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.
Contribute
We welcome contributions to improve this project! Before submitting a pull request, please:
- Add appropriate unit tests in the
testsdirectory - Run
python -m pytestto ensure all tests pass - Submit your PR with clear descriptions of the changes
Acknowledgements
- Built with MCP Python SDK
FAQ
- What is the Python Code Interpreter MCP server?
- Python Code Interpreter is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Python Code Interpreter?
- This profile displays 58 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.7★★★★★58 reviews- ★★★★★Tariq Thomas· Dec 28, 2024
Strong directory entry: Python Code Interpreter surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Tariq Anderson· Dec 24, 2024
Useful MCP listing: Python Code Interpreter is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Maya Gupta· Dec 20, 2024
According to our notes, Python Code Interpreter benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Ishan Sethi· Dec 12, 2024
I recommend Python Code Interpreter for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Ganesh Mohane· Dec 8, 2024
Useful MCP listing: Python Code Interpreter is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Zara Ndlovu· Dec 8, 2024
According to our notes, Python Code Interpreter benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Rahul Santra· Nov 27, 2024
Python Code Interpreter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ira Tandon· Nov 27, 2024
Python Code Interpreter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Evelyn Thompson· Nov 15, 2024
Python Code Interpreter reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Hiroshi Menon· Nov 11, 2024
Python Code Interpreter is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
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