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MCP Code Executor Server

@mouadenna

代码仓库

基本信息

分类

开发工具

许可证

MIT license

传输方式

stdio

作者

mouadenna

关于 MCP Code Executor Server

dedicated isolated environment for your AI agent

MCP Code Executor 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": {
    "mcp-code-executor-server": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "mcp-code-executor",
        "."
      ]
    }
  }
}

MCP Code Executor 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 Mcp Code Executor Server?

Mcp Code Executor Server is a Model Context Protocol (MCP) server that enables AI agents to execute code across multiple programming languages in a secure, isolated environment. It standardizes how AI models connect to various programming runtimes by exposing a unified MCP tool endpoint. The server is for developers integrating code execution capabilities into AI workflows.

How to use Mcp Code Executor Server?

Clone the repository, build with ./mvnw clean package, and start the server with ./mvnw spring-boot:run. The server listens on port 8080 by default. Alternatively, run via Docker using docker build -t mcp-code-executor . and docker run -p 8080:8080 mcp-code-executor. Connect an MCP-compatible AI client to http://localhost:8080 and invoke the code-execution tool with a JSON payload specifying language and code.

Key features of Mcp Code Executor Server

  • Multi-language support: Java, Python, JavaScript, TypeScript, C++
  • Secure execution in isolated containers with resource limits
  • MCP integration for seamless client discovery
  • Automatic compilation for Java, C++, and TypeScript
  • Detailed compile-time and runtime error feedback
  • Automatic cleanup of temporary files and execution timeouts

Use cases of Mcp Code Executor Server

  • AI assistants executing code snippets in a sandboxed environment
  • Automated testing of code in multiple languages from a single interface
  • Providing AI agents with runtime feedback to refine code generation
  • Enabling multi-language code evaluation in educational AI tools
  • Integrating code execution into MCP-compatible client workflows

常见问题

1

Which programming languages are supported?

Java, Python, JavaScript, TypeScript, and C++.

2

How does the server ensure secure code execution?

It uses isolated containers with resource limits on CPU, memory, and timeouts, and automatically deletes temporary files.

3

What are the prerequisites to run the server locally?

Java 17+, Python 3.8+, Node.js & npm, and a C++ compiler (g++).

4

Can I run the server in a container for additional isolation?

Yes, the README provides Docker and Docker Compose instructions for running the server in a container.

5

How do I connect an MCP client to the server?

Start your MCP-compatible AI client and point it to `http://localhost:8080` to discover and invoke the `code-execution` tool.

评论

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