iflow-mcp_sjtu-sai-agents-mcp_sandbox

# Code Execution Server This repository provides a basic implementation of a **code execution server**, designed primarily for **Xmaster** ([paper](https://arxiv.org/abs/2507.05241), [code](https://github.com/sjtu-sai-agents/X-Master)) and **Browse Master** ([paper](https://arxiv.org/abs/2508.09129) [code](https://github.com/sjtu-sai-agents/Browse-Master)). The full implementation is used in [SciMaster](https://scimaster.bohrium.com). Due to the proprietary nature of the full code, this repository only includes an **open-source framework** and the **basic components** required for code execution. It also includes a simple **network search tool** implementation. > **⚠️ Warning**: This is a basic code execution server without virtualization or safety protections. For added security, consider running it within **Docker** or **Apptainer** containers as necessary. --- ## 🛠️ Setup ### Environment Clone this repository and navigate to the project directory and install the required dependencies: ```bash cd mcp_sandbox/ pip install -r requirements.txt ``` ### Tools - setup the serper key in `configs/web_agent.json` - setup the models' api key in `configs/llm_call.json` --- ## 🚀 Deploy the Code Execution Server ### Step 1: Start the API Server We will first start the API server used by the tools. This API server proxies all search-related services, including: - [Serper](https://serper.dev/)'s Google Search Service - A series of Model APIs Navigate to the api_proxy directory and start the API server: ```bash cd api_proxy python api_server.py ``` ### Step 2: Deploy the Server Deploy the server by running the following script in the `MCP` directory: ```bash cd MCP bash deploy_server.sh ``` --- ## 📝 Usage ### Sending a Request To send a request to the server, use the following `curl` command: ```bash curl -X POST "http://<your-server-url>/execute" \ -H "Content-Type: application/json" \ -d '{"code": "<your code here>"}' ``` ### ⚡ Benchmarki…