ipybox

# ipybox mcp-name: io.github.gradion-ai/ipybox <p align="left"> <a href="https://gradion-ai.github.io/ipybox/"><img alt="Website" src="https://img.shields.io/website?url=https%3A%2F%2Fgradion-ai.github.io%2Fipybox%2F&up_message=online&down_message=offline&label=docs"></a> <a href="https://pypi.org/project/ipybox/"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/ipybox?color=blue"></a> <a href="https://github.com/gradion-ai/ipybox/releases"><img alt="GitHub Release" src="https://img.shields.io/github/v/release/gradion-ai/ipybox"></a> <a href="https://github.com/gradion-ai/ipybox/actions"><img alt="GitHub Actions Workflow Status" src="https://img.shields.io/github/actions/workflow/status/gradion-ai/ipybox/test.yml"></a> <a href="https://github.com/gradion-ai/ipybox/blob/main/LICENSE"><img alt="GitHub License" src="https://img.shields.io/github/license/gradion-ai/ipybox?color=blueviolet"></a> </p> > [!NOTE] > **Next generation ipybox** > > This is the next generation of ipybox, a complete rewrite. Older versions are maintained on the [0.6.x branch](https://github.com/gradion-ai/ipybox/tree/0.6.x) and can be obtained with `pip install ipybox<0.7`. [ipybox](https://gradion-ai.github.io/ipybox/) is a Python code execution sandbox with first-class support for programmatic MCP tool calling. It generates a typed Python tool API from MCP server tool schemas, supporting both local stdio and remote HTTP servers. Code that calls the generated API executes in a sandboxed IPython kernel, providing a stateful environment where variables and definitions persist across executions. The generated API delegates MCP tool execution to a separate environment that enforces tool call approval, requiring applications to explicitly accept or reject each tool call before it executes.  *`CodeExecutor` coordinates sandboxed code execution, tool execution, and tool call approval.* ## Agent integration ipybox is designed for agents that interact with their environment through [code actions](https://arxiv.org/abs/2402.01030) rather than JSON tool calls, a more reliable approach since LLMs are heavily pretrained on Python code compared to JSON tool call post-training. Agents generate and execute Python code that composes multiple MCP tool calls into a single action, using loops, conditionals, and data transformations that keep intermediate results out of the agent's context window. Since agent-generated code cannot be trusted, it must run in a secure sandboxed environment, and all MCP tool calls must be approved by the application. ipybox supports both with minimal setup. ## Features - **Stateful code execution** — state persists across executions in IPython kernels - **Lightweight sandboxing** — kernel isolation via Anthropic's [sandbox-runtime](https://github.com/anthropic-experimental/sandbox-runtime) - **Generated Python tool API** — functions and models generated from MCP tool schemas - **Programmatic MCP tool calling** — MCP tools called via Python code, not JSON directly - **MCP tool call approval** — every MCP tool call requires application-level approval - **Any MCP server** — supports stdio, Streamable HTTP, and SSE transports - **Any Python package** — install and use any Python package in IPython kernels - **Local code execution** — no cloud dependencies, everything runs on your machine - **Python SDK and MCP server** — use ipybox programmatically or as an MCP server - **Claude Code plugin** — a plugin for [programmatic tool calling in Claude Code](https://gradion-ai.github.io/ipybox/ccplugin/) ## Documentation See the [documentation](https://gradion-ai.github.io/ipybox/) for installation instructions, quickstart guide, and detailed usage information. For LLM-friendly documentation, see [llms.txt](https://gradion-ai.github.io/ipybox/llms.txt) and [llms-full.txt](https://gradion-ai.github.io/ipybox/llms-full.txt).