mcp-pyrunner 0.1.2

Universal Python code execution MCP server - one tool to rule them all

MCP Code Mode 🐍⚡

Universal Python code execution MCP server - one tool to rule them all.

Inspired by Cloudflare's Code Mode: LLMs are better at writing code than making tool calls because they've trained on millions of real repositories.

Why Code Mode?

Traditional approach (many tools):

User: "Get weather for Austin and save to file"

LLM: [tool_call: get_weather(location="Austin")]
     → waits for response...
LLM: [tool_call: write_file(path="weather.txt", content=...)]
     → waits for response...

Code Mode approach (one tool):

User: "Get weather for Austin and save to file"

LLM: [run_python]
import requests
weather = requests.get("https://wttr.in/Austin?format=j1").json()
temp = weather['current_condition'][0]['temp_F']
with open("weather.txt", "w") as f:
    f.write(f"Austin: {temp}°F")
print(f"Saved! Temperature: {temp}°F")

Benefits

Traditional Tools	Code Mode
❌ LLMs struggle with synthetic tool-call format	✅ LLMs excel at writing real code
❌ Each tool call = round trip to LLM	✅ Complex workflows in one execution
❌ Managing 20+ extensions	✅ One universal tool
❌ Token waste passing data between calls	✅ Efficient data flow in code
❌ Limited to pre-built capabilities	✅ Anything Python can do

Works With Any MCP Client

• ✅ Goose (Block's AI agent)
• ✅ Claude Desktop
• ✅ Cursor
• ✅ VS Code with Copilot
• ✅ Any MCP-compatible agent

Features

🚀 Universal Execution

Write Python to accomplish any task - HTTP requests, file operations, data processing, web scraping, image manipulation, and more.

📦 Auto-Install Dependencies

Missing a package? Code Mode detects ModuleNotFoundError, installs the package, and retries automatically.

🌊 Streaming Output (NEW!)

See results in real-time! run_python_stream shows output line-by-line as your code executes. Perfect for long-running tasks, progress bars, and monitoring live operations.

🧠 Learning System

Records error patterns and solutions. Future executions benefit from past learnings. Persists across sessions.

🔄 Intelligent Retry

run_with_retry analyzes failures and suggests fixes based on error patterns and past learnings.

🐳 Optional Docker Sandbox

Run code in isolated Docker containers for enhanced security.

⚙️ Configurable

Adjust timeouts, execution modes, package restrictions, and more.

Installation

From PyPI (when published)

# Using uv (recommended)
uv tool install mcp-pyrunner

# Using pip
pip install mcp-pyrunner

From Source

git clone https://github.com/anaseqal/codemode.git
cd codemode
uv sync

Configuration

Goose

If installed from PyPI:

Edit ~/.config/goose/config.yaml:

extensions:
  codemode:
    type: stdio
    enabled: true
    cmd: uvx
    args: ["mcp-pyrunner"]

If running from source (local development):

extensions:
  codemode:
    type: stdio
    enabled: true
    cmd: uv
    args: ["run", "--directory", "/path/to/codemode", "mcp-pyrunner"]
    # Replace /path/to/codemode with actual path (e.g., ~/codemode)

Or use the UI: Extensions → Add Custom Extension → STDIO → Command: uv run --directory /path/to/codemode mcp-pyrunner

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

If installed from PyPI:

{
  "mcpServers": {
    "codemode": {
      "command": "uvx",
      "args": ["mcp-pyrunner"]
    }
  }
}

If running from source:

{
  "mcpServers": {
    "codemode": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/codemode", "mcp-pyrunner"]
    }
  }
}

Cursor

Add to .cursor/mcp.json:

If installed from PyPI:

{
  "mcpServers": {
    "codemode": {
      "command": "uvx",
      "args": ["mcp-pyrunner"]
    }
  }
}

If running from source:

{
  "mcpServers": {
    "codemode": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/codemode", "mcp-pyrunner"]
    }
  }
}

Available Tools

Tool	Description
get_system_context	Get environment info before writing code
run_python	Execute Python code (auto-installs packages)
run_python_stream	Execute with real-time streaming output
run_with_retry	Execute with intelligent retry and error analysis
add_learning	Record solutions for future reference
get_learnings	View/search past learnings
pip_install	Pre-install a specific package
configure	View/update settings

Usage Examples

Web Scraping

User: "Scrape the top 10 posts from Hacker News"

→ run_python:
import requests
from bs4 import BeautifulSoup

resp = requests.get("https://news.ycombinator.com")
soup = BeautifulSoup(resp.text, "html.parser")

for i, item in enumerate(soup.select(".titleline > a")[:10], 1):
    print(f"{i}. {item.text}")
    print(f"   {item['href']}\n")

Data Processing

User: "Analyze sales.csv and show monthly totals"

→ run_python:
import pandas as pd

df = pd.read_csv("sales.csv")
df["date"] = pd.to_datetime(df["date"])
monthly = df.groupby(df["date"].dt.to_period("M"))["amount"].sum()

print("Monthly Sales:")
for period, total in monthly.items():
    print(f"  {period}: ${total:,.2f}")

API Integration

User: "Get the current Bitcoin price in USD"

→ run_python:
import requests

data = requests.get("https://api.coinbase.com/v2/prices/BTC-USD/spot").json()
price = float(data["data"]["amount"])
print(f"Bitcoin: ${price:,.2f} USD")

Image Processing

User: "Resize all images in ./photos to 800x600"

→ run_python:
from pathlib import Path
from PIL import Image

photos = Path("./photos")
for img_path in photos.glob("*.jpg"):
    img = Image.open(img_path)
    img.thumbnail((800, 600))
    img.save(img_path)
    print(f"Resized: {img_path.name}")

Streaming Output (Real-Time Progress)

User: "Scrape top 20 HN posts with progress updates"

→ run_python_stream:
import requests
from bs4 import BeautifulSoup
import time

print("🔍 Starting to scrape Hacker News...")

resp = requests.get("https://news.ycombinator.com")
soup = BeautifulSoup(resp.text, "html.parser")
stories = soup.select(".titleline > a")[:20]

print(f"📊 Found {len(stories)} stories. Processing...\n")

for i, story in enumerate(stories, 1):
    # Show progress in real-time
    progress = "█" * i + "░" * (20 - i)
    print(f"[{progress}] {i}/20: {story.text}")
    time.sleep(0.5)  # See each item appear live!

print("\n✅ Scraping complete!")

# Output appears LINE BY LINE as the code runs,
# not all at once at the end!

Configuration Options

View current config:

→ configure()

Update settings:

→ configure(action="set", key="execution_mode", value="docker")
→ configure(action="set", key="default_timeout", value="120")

Setting	Values	Description
execution_mode	direct, docker	How to run code
default_timeout	integer	Default timeout (seconds)
max_retries	integer	Default retry attempts
auto_install	true, false	Auto-install packages
docker_image	string	Docker image for sandbox

Learning System

When you solve an error, record it:

→ add_learning(
    error_pattern="SSL: CERTIFICATE_VERIFY_FAILED",
    solution="Use verify=False or install/update certifi",
    context="HTTPS requests on systems with cert issues",
    tags="ssl,https,certificates"
)

View learnings:

→ get_learnings()
→ get_learnings(search="ssl")

Learnings persist in ~/.mcp-pyrunner/learnings.json and improve future executions.

Data Storage

Code Mode stores data in ~/.mcp-pyrunner/:

~/.mcp-pyrunner/
├── config.json       # User configuration
├── learnings.json    # Error patterns and solutions
└── execution_log.json # Recent execution history

Security Considerations

⚠️ Code Mode executes arbitrary Python code.

Direct mode (default):

• Code runs with your user permissions
• Full filesystem and network access
• Fast execution

Docker mode (more secure):

• Code runs in isolated container
• Limited resources (512MB RAM, 1 CPU)
• Network access available
• Slower startup

Enable Docker mode:

→ configure(action="set", key="execution_mode", value="docker")

Testing

# Run tests
uv run pytest

# Test with MCP Inspector
uv run mcp dev src/mcp_codemode/server.py
# Open http://localhost:5173

Contributing

Contributions welcome! Areas of interest:

• Streaming output for long-running code ✅ DONE!
• Vector DB for semantic learning search
• Pyodide/WASM sandboxing option
• Code analysis before execution
• Resource usage tracking
• Multi-file project support

License

MIT

Acknowledgments

• Cloudflare's Code Mode for the inspiration
• Model Context Protocol for the standard
• Block's Goose for being an excellent MCP client