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CodeBox AI MCP Server

@tomconte

代码仓库

基本信息

分类

AI 与智能体

许可证

MIT

传输方式

stdio

作者

tomconte

关于 CodeBox AI MCP Server

A secure Python code execution service designed to integrate with LLMs like GPT and Claude, providing a self-hosted alternative to OpenAI's Code Interpreter. Now with MCP server.

CodeBox AI MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "codebox-ai": {
      "command": "uv",
      "args": [
        "run",
        "mcp",
        "dev",
        "mcp_server.py"
      ]
    }
  }
}

CodeBox AI MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is CodeBox-AI?

CodeBox-AI is a secure, self-hosted Python code execution service built with FastAPI and IPython kernels. It provides a private alternative to OpenAI’s Code Interpreter or Anthropic’s Claude analysis tool, supports session-based code execution, and integrates with LLM function calling and the Model Context Protocol (MCP).

How to use CodeBox-AI?

Clone the repository, install dependencies with uv sync, and start the server with uv run -m codeboxai.main. Optionally run the MCP server standalone with uv run mcp dev mcp_server.py, register it with Claude Desktop via uv run mcp install mcp_server.py --name "CodeBox-AI", or start both FastAPI and MCP servers together with uv run run.py. Use the REST API endpoints (POST /sessions, POST /execute, etc.) or the MCP tools (execute_code, session://{session_id}, sessions://) for code execution.

Key features of CodeBox-AI

  • Session-based Python code execution in Docker containers
  • IPython kernel for rich output and plotting support
  • Dynamic package installation with allowlist/blocklist and version controls
  • State persistence between executions in the same session
  • AST-based code security validation and dangerous import protection
  • Host directory mounting with read-only or read-write access control

Use cases of CodeBox-AI

  • Give an LLM (like Claude or GPT) a secure Python sandbox for data analysis
  • Run multi-step computational workflows that keep variables across cells
  • Allow users to safely execute untrusted code inside isolated Docker sessions
  • Enable LLM applications to mount local directories for file processing tasks

常见问题

1

What runtime dependencies does CodeBox-AI require?

Python 3.9+, Docker, and [uv](https://github.com/astral-sh/uv) (fast package installer and resolver). For MCP or Claude Desktop integration, `uv` must be installed and configured.

2

How does CodeBox-AI keep code execution secure?

Code execution runs inside Docker containers – each session is isolated. The service implements AST-based code analysis, protection against dangerous imports and operations, basic resource limits, and a package allowlist/blocklist system.

3

Can I mount local directories into the execution environment?

Yes. You can specify mount points with host and container paths, set read-only or read-write access, and the service performs security validations to prevent access to sensitive paths. Mounting can be done via the API or environment variable `CODEBOX_MOUNT_PATH`.

4

Does CodeBox-AI support integration with Claude Desktop?

Yes. You can register the MCP server with Claude Desktop by installing it via `uv run mcp install mcp_server.py --name "CodeBox-AI"` and adding the server configuration to the Claude Desktop settings file (`claude_desktop_config.json`). The server then appears as a custom tool.

5

Is this code ready for production use?

No – the README explicitly states it is “a prototype implementation, not intended for production use without additional hardening.” It provides basic security and resource limits but should be reviewed and extended for production scenarios.

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