MCP Run Python
Code is executed using Pyodide in Deno and is therefore isolated from the rest of the operating system.
Features
- Secure Execution: Run Python code in a sandboxed WebAssembly environment
- Package Management: Automatically detects and installs required dependencies
- Complete Results: Captures standard output, standard error, and return values
- Asynchronous Support: Runs async code properly
- Error Handling: Provides detailed error reports for debugging
(This code was previously part of Pydantic AI but was moved to a separate repo to make it easier to maintain.)
Usage
To use this server, you must have both Python and Deno installed.
The server can be run with deno installed using uvx:
uvx mcp-run-python [-h] [--version] [--port PORT] [--deps DEPS] {stdio,streamable-http,streamable-http-stateless,example}
where:
stdioruns the server with the Stdio MCP transport — suitable for running the process as a subprocess locallystreamable-httpruns the server with the Streamable HTTP MCP transport - suitable for running the server as an HTTP server to connect locally or remotely. This supports stateful requests, but does not require the client to hold a stateful connection like SSEstreamable-http-statelessruns the server with Streamable HTTP MCP transport in stateless mode and does not support server-to-client notificationsexamplewill run a minimal Python script usingnumpy, useful for checking that the package is working, for the code to run successfully, you'll need to installnumpyusinguvx mcp-run-python --deps numpy example
Usage with Pydantic AI
Then you can use mcp-run-python with Pydantic AI:
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStdio
from mcp_run_python import deno_args_prepare
import logfire
logfire.configure()
logfire.instrument_mcp()
logfire.instrument_pydantic_ai()
server = MCPServerStdio('uvx', args=['mcp-run-python@latest', 'stdio'], timeout=10)
agent = Agent('claude-3-5-haiku-latest', toolsets=[server])
async def main():
async with agent:
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.w
if __name__ == '__main__':
import asyncio
asyncio.run(main())
Usage in codes as an MCP server
First install the mcp-run-python package:
pip install mcp-run-python
# or
uv add mcp-run-python
With mcp-run-python installed, you can also run deno directly with prepare_deno_env or async_prepare_deno_env
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStdio
from mcp_run_python import async_prepare_deno_env
import logfire
logfire.configure()
logfire.instrument_mcp()
logfire.instrument_pydantic_ai()
async def main():
async with async_prepare_deno_env('stdio') as deno_env:
server = MCPServerStdio('deno', args=deno_env.args, cwd=deno_env.cwd, timeout=10)
agent = Agent('claude-3-5-haiku-latest', toolsets=[server])
async with agent:
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.w
if __name__ == '__main__':
import asyncio
asyncio.run(main())
Note: prepare_deno_env can take deps as a keyword argument to install dependencies.
As well as returning the args needed to run mcp_run_python, prepare_deno_env creates a new deno environment
and installs the dependencies so they can be used by the server.
Usage in code with code_sandbox
mcp-run-python includes a helper function code_sandbox to allow you to easily run code in a sandbox.
from mcp_run_python import code_sandbox
code = """
import numpy
a = numpy.array([1, 2, 3])
print(a)
a
"""
async def main():
async with code_sandbox(dependencies=['numpy']) as sandbox:
result = await sandbox.eval(code)
print(result)
if __name__ == '__main__':
import asyncio
asyncio.run(main())
Under the hood, code_sandbox runs an MCP server using stdio. You can run multiple code blocks with a single sandbox.
Logging
MCP Run Python supports emitting stdout and stderr from the python execution as MCP logging messages.
For logs to be emitted you must set the logging level when connecting to the server. By default, the log level is set to the highest level, emergency.
Dependencies
mcp_run_python uses a two step process to install dependencies while avoiding any risk that sandboxed code can
edit the filesystem.
denois first run with write permissions to thenode_modulesdirectory and dependencies are installed, causing wheels to be written to ``denois then run with read-only permissions to thenode_modulesdirectory to run untrusted code.
Dependencies must be provided when initializing the server so they can be installed in the first step.