Python Sandbox MCP server

This MCP server enables you to safely execute Python code in a sandboxed environment. It uses Pyodide running in Deno to isolate the execution from your operating system, providing a secure way to run Python code within your applications.

Installation

To use the MCP Run Python server, you need to have Deno installed on your system. Once Deno is set up, you can run the server using the following command:

deno run \
  -N -R=node_modules -W=node_modules --node-modules-dir=auto \
  jsr:@pydantic/mcp-run-python [stdio|sse|warmup]

The command arguments include:

• -N -R=node_modules -W=node_modules: Provides network access and read/write permissions to the ./node_modules directory
• --node-modules-dir=auto: Configures Deno to use a local node_modules directory
• The last parameter specifies the transport method:
  • stdio: Runs the server using Stdio MCP transport (suitable for subprocess usage)
  • sse: Runs the server as an HTTP server with SSE transport (for local or remote connections)
  • warmup: Downloads and caches the Python standard library (useful for initial setup)

Usage

Warming Up the Server

Before using the server in production, it's recommended to warm it up to cache the Python standard library:

deno run \
  -N -R=node_modules -W=node_modules --node-modules-dir=auto \
  jsr:@pydantic/mcp-run-python warmup

Using with PydanticAI

Here's how to integrate the MCP Run Python server with PydanticAI:

from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStdio

import logfire

logfire.configure()
logfire.instrument_mcp()
logfire.instrument_pydantic_ai()

server = MCPServerStdio('deno',
    args=[
        'run',
        '-N',
        '-R=node_modules',
        '-W=node_modules',
        '--node-modules-dir=auto',
        'jsr:@pydantic/mcp-run-python',
        'stdio',
    ])
agent = Agent('claude-3-5-haiku-latest', mcp_servers=[server])


async def main():
    async with agent.run_mcp_servers():
        result = await agent.run('How many days between 2000-01-01 and 2025-03-18?')
    print(result.output)
    #> There are 9,208 days between January 1, 2000, and March 18, 2025.

if __name__ == '__main__':
    import asyncio
    asyncio.run(main())

This example shows how to:

1. Configure a server using MCPServerStdio
2. Create an agent that uses the server
3. Run a query that will execute Python code through the MCP server

For complete documentation, visit the official documentation at https://ai.pydantic.dev/mcp/run-python/.

How to add this MCP server to Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > MCP and click "Add new global MCP server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "cursor-rules-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "cursor-rules-mcp"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explictly ask the agent to use the tool by mentioning the tool name and describing what the function does.