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<h1>MCP Run Python</h1>
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MCP server to run Python code in a sandbox.
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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.
