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Anyrun MCP Server

@appcom2016

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

appcom2016

提交者

appcom2016

关于 Anyrun MCP Server

暂无概览

#docker#sandbox#python#code-execution#ai-agent

Anyrun 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": {
    "anyrun": {
      "command": "python3",
      "args": [
        "-m",
        "anyrun.mcp_server"
      ]
    }
  }
}

Anyrun 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 Anyrun?

Anyrun is a Docker sandbox execution engine for AI Agents. It provides secure, isolated code execution with full observability — automatically collecting execution traces, discovering error patterns and success paths, and extracting reusable experience as SKILL.md via LLM, with autonomous skill evolution.

How to use Anyrun?

Run any code securely with a single line of Python. All tools (sandbox_run, file operations, toolbox management) are exposed via the MCP protocol.

Key features of Anyrun

  • Zero-config Docker sandbox: one line of Python
  • Full observability: traces stored to SQLite + JSON
  • Pattern discovery: error clustering and anomaly detection
  • LLM auto-generates SKILL.md from execution patterns
  • Self-evolution: skills auto-upgrade, decay, and repair

Use cases of Anyrun

  • AI Agent tool execution
  • Secure code sandboxing
  • Agent self-improvement pipelines
  • CI/CD code execution

常见问题

1

What runtime does Anyrun require?

Anyrun requires Docker for sandbox execution and Python for client integration.

2

How does Anyrun store execution data?

Every execution trace is stored to SQLite and JSON for full observability.

3

What protocol does Anyrun use to expose tools?

Anyrun exposes all tools (sandbox_run, file ops, toolbox management) natively via the MCP protocol.

4

Can Anyrun evolve its skills automatically?

Yes, skills auto-upgrade, decay, and get repaired as part of the self-evolution feature.

5

What is SKILL.md?

SKILL.md is a file containing reusable experience automatically extracted from execution patterns by the LLM.

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