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...

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")

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

# Using pip
pip install mcp-pyrunner

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

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

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)

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

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

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

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

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")

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}")

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")

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}")

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!

User: "Take a screenshot of example.com and create a summary report"

→ run_python:
from playwright.sync_api import sync_playwright
import json

with sync_playwright() as p:
    browser = p.chromium.launch()
    page = browser.new_page()
    page.goto("https://example.com")

    # Take screenshot
    screenshot_path = "/tmp/example_screenshot.png"
    page.screenshot(path=screenshot_path)

    # Create report
    report = {
        "url": "https://example.com",
        "title": page.title(),
        "screenshot": screenshot_path,
        "timestamp": "2025-01-26T12:00:00"
    }

    report_path = "/tmp/report.json"
    with open(report_path, "w") as f:
        json.dump(report, f, indent=2)

    browser.close()

    # Print file paths - Code Mode auto-detects and displays them!
    print(f"Screenshot saved to: {screenshot_path}")
    print(f"Report saved to: {report_path}")

# Result: Your MCP client displays the screenshot IMAGE inline
# and shows the JSON content formatted - no manual handling needed!

→ configure()

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

→ 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"
)

→ record_semantic_failure(
    objective="Display image in Goose app",
    failed_approach="Used print() to output file path",
    successful_approach="Returned base64 encoded image as MCP content object",
    context="MCP clients need structured content objects, not just paths",
    tags="goose,mcp,display,images"
)

→ get_learnings()              # View all learnings (error + semantic)
→ get_learnings(search="ssl")  # Search learnings

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

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

# Run tests
uv run pytest

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