MCP Python Executor
About
# 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 -…
Details
- License
- MIT license
Explore
- 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
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 ExecutorCommand (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
From the repository
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"
}
}
}
}
- 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)
Install Python packages.
interface InstallPackagesArgs {
packages: string[];
}
Example:
```
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"
}
}
}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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