MCP Run Python

MCP server to run Python code in a sandbox.

--- # NOTICE We've decided to retire and archive this project - there's just no safe way to run Python within pyodide safely with reasonable latency. Instead, we're working hard on [Monty](https://github.com/pydantic/monty) which should solve the usecase we initially intended for `mcp-run-python`, with better security, lower latency, easier install, and better ways to communicate with the OS. If you want to use this projects code, or otherwise use pyodide to run LLM generated code, feel free to do so. However be extremely careful about how you sandbox the service and what code you allow to run. In particular **Python code running in pyodide can run arbitrary javascript** meaning it can do whatever the javascript runtime running pydodie can do, including: * tainting that runtime to control or alter how code runs on later onvocations * reading and/or writing to any files that runtime has access to * OOMing the machine by consuming all memory - deno has no good way limit memory usage These issues are not problems with Pyodide or Deno - they're behaving as advertised, it's just that those tools were not designed as sandboxes to run untrusted code. --- Code is executed using [Pyodide](https://pyodide.org) in [Deno](https://deno.com/) 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](https://github.com/pydantic/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](https://deno.com/) installed. The server can be run with `deno` installed using `uvx`: ```bash uvx mcp-run-python [-h] [--version] [--port PORT] [--deps DEPS] {stdio,streamable-http,streamable-http-stateless,example} ``` where: - `stdio` runs the server with the [Stdio MCP transport](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#stdio) — suitable for running the process as a subprocess locally - `streamable-http` runs the server with the [Streamable HTTP MCP transport](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http) - 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 SSE - `streamable-http-stateless` runs the server with [Streamable HTTP MCP transport](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http) in stateless mode and does not support server-to-client notifications - `example` will run a minimal Python script using `numpy`, useful for checking that the package is working, for the code to run successfully, you'll need to install `numpy` using `uvx mcp-run-python --deps numpy example` ## Usage with Pydantic AI Then you can use `mcp-run-python` with Pydantic AI: ```python 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: ```bash 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` ```python 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. ```py 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](https://github.com/modelcontextprotocol/specification/blob/eb4abdf2bb91e0d5afd94510741eadd416982350/docs/specification/draft/server/utilities/logging.md?plain=1). 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. * `deno` is first run with write permissions to the `node_modules` directory and dependencies are installed, causing wheels to be written to `` * `deno` is then run with read-only permissions to the `node_modules` directory to run untrusted code. Dependencies must be provided when initializing the server so they can be installed in the first step.

Mcp Run Python

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Mcp Run Python

Install