title: Run My Script
emoji: 🏃
colorFrom: red
colorTo: indigo
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: false
tags:
- mcp-server-track
license: mit
short_description: creates a venv, install dependencies and run your script
test video using Claude https://youtu.be/2jC4Kgqa5O4
image generated by the code produced in the video
run_my_script
Executes a user-provided self contained Python script inside an isolated virtual environment with automatic dependency management.
This function is intended to serve as a backend execution engine in a Model Context Protocol (MCP) server setting,
where a language model may submit scripts for evaluation. It creates a secure workspace, detects dependencies,
installs them using uv, executes the script, captures its output (including stdout and generated files), and
returns all relevant results.
⚠️ Limitations & Guidance for mcp server Use:
- Scripts should be self-contained, avoid system-level access, and primarily focus on data processing, text generation, visualization, or machine learning tasks.
- The code can output logs, JSON, images, CSVs, or any other files, which are returned as artifacts.
- Avoid infinite loops or long-running background processes. Timeout support can be added externally.
Args: code (str): The Python script to execute. Should include all import statements and logic.
user_input (str, optional): A string input available to the script via the SCRIPT_INPUT environment variable.
Can be plain text, JSON, Markdown, or even base64-encoded images.
Returns: Tuple[str, Dict[str, str], str]: - logs (str): Full stdout and stderr logs of the executed script. - artifacts (Dict[str, str]): A dictionary of output files with their names and summaries or indicators (e.g., image or CSV placeholders). Includes a "workdir" key pointing to the working directory. - zip_path (str): Path to a ZIP archive containing all output artifacts for download.
test script:
import os
import base64
from io import BytesIO
from PIL import Image
import json
#input_path = os.environ["SCRIPT_INPUT"]
#output_path = os.environ["SCRIPT_OUTPUT"]
# Load JSON input
with open("input.txt", "r") as f:
data = json.load(f)
img_b64 = data["img"]
# Decode base64 to image
img_bytes = base64.b64decode(img_b64)
img = Image.open(BytesIO(img_bytes))
img.load() # ⬅️ Ensure image is fully loaded before processing
# Flip image horizontally
flipped = img.transpose(Image.FLIP_LEFT_RIGHT)
# Save output
flipped.save("flipped.png")
print("Image flipped and saved as flipped.png.")
test input:
{
"img": "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"
}