MCP Python Executor

by bsmi021

390 downloads

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Description

# MCP Python Executor A Model Context Protocol (MCP) server for executing Python code and managing Python packages. ## Features - Execute Python code with safety constraints - Install and manage Python packages - Pre-configure commonly used packages - Resource monitoring and…

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Author: bsmi021
Downloads: 390
Categories: Other

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name MCP Python Executor

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

From the repository

Configure the server in your MCP settings with the command node path/to/python-executor/build/index.js. Customize behavior via environment variables such as PREINSTALLED_PACKAGES, MAX_MEMORY_MB, EXECUTION_TIMEOUT_MS, and MAX_CONCURRENT_EXECUTIONS. Then use the provided tools—execute_python to run inline or file-based Python code, and install_packages to install Python packages.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "mcp python executor": {
            "mcp-python-executor": {
                "command": "node",
                "args": [
                    "path/to/python-executor/build/index.js"
                ],
                "env": {
                    "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
                    "MAX_MEMORY_MB": "512",
                    "EXECUTION_TIMEOUT_MS": "30000",
                    "MAX_CONCURRENT_EXECUTIONS": "5",
                    "LOG_LEVEL": "info",
                    "LOG_FORMAT": "json"
                }
            }
        }
    }
}

McpServers

{
    "mcp-python-executor": {
        "command": "node",
        "args": [
            "path/to/python-executor/build/index.js"
        ],
        "env": {
            "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
            "MAX_MEMORY_MB": "512",
            "EXECUTION_TIMEOUT_MS": "30000",
            "MAX_CONCURRENT_EXECUTIONS": "5",
            "LOG_LEVEL": "info",
            "LOG_FORMAT": "json"
        }
    }
}

MCP Python Executor

A Model Context Protocol (MCP) server for executing Python code and managing Python packages.

Features

- Execute Python code with safety constraints
- Install and manage Python packages
- Pre-configure commonly used packages
- Resource monitoring and limits
- Health checks and metrics
- Structured logging

Configuration

The server can be configured through environment variables in the MCP settings:

{
  "mcpServers": {
    "mcp-python-executor": {
      "command": "node",
      "args": ["path/to/python-executor/build/index.js"],
      "env": {
        "PREINSTALLED_PACKAGES": "numpy pandas matplotlib scikit-learn",
        "MAX_MEMORY_MB": "512",
        "EXECUTION_TIMEOUT_MS": "30000",
        "MAX_CONCURRENT_EXECUTIONS": "5",
        "LOG_LEVEL": "info",
        "LOG_FORMAT": "json"
      }
    }
  }
}

Environment Variables

- PREINSTALLED_PACKAGES: Space-separated list of Python packages to install on startup
- MAX_MEMORY_MB: Maximum memory limit per execution (default: 512)
- EXECUTION_TIMEOUT_MS: Maximum execution time in milliseconds (default: 30000)
- MAX_CONCURRENT_EXECUTIONS: Maximum number of concurrent executions (default: 5)
- LOG_LEVEL: Logging level (debug|info|error, default: info)
- LOG_FORMAT: Log format (json|text, default: json)

Available Tools

1. execute_python

Execute Python code and return the results.

interface ExecutePythonArgs {
  code?: string;          // Python code to execute (inline)
  scriptPath?: string;    // Path to existing Python script file (alternative to code)
  inputData?: string[];   // Optional input data
}

Examples:

// Example with inline code
{
  "code": "print('Hello, World!!')\nfor i in range(3): print(i)",
  "inputData": ["optional", "input", "data"]
}
// Example with script path

{

  "scriptPath": "/path/to/your_script.py",

  "inputData": ["optional", "input", "data"]

}

2. install_packages

Install Python packages.

interface InstallPackagesArgs {
  packages: string[];
}

Example:

```

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Description

About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

MCP Python Executor

Features

Configuration

Environment Variables

Available Tools

1. execute_python

2. install_packages

Install

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