原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
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Agent Workspace MCP

作者:HrRodan版本 1.4.134i★ –i在你的电脑上运行
注册名称io.github.HrRodan/agent-workspace-mcp

安装

概览

作者提供的说明(英文原文)

A sandboxed, agentic workspace providing secure filesystem, bash, and uv-powered Python execution.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 开发者工具

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "agent-workspace": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/hrrodan/agent-workspace-mcp:1.4.134"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add agent-workspace -- docker run -i --rm ghcr.io/hrrodan/agent-workspace-mcp:1.4.134

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "agent-workspace": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/hrrodan/agent-workspace-mcp:1.4.134"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "agent-workspace": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/hrrodan/agent-workspace-mcp:1.4.134"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 HrRodan 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“开发者工具”中的其他服务器比较i

  • 星标数多于此分类中其他 4923 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 4923 个服务器的 85% 更新
  • 基础检查结果等于或优于此分类中其他 4923 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Agent Workspace MCP 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(1.4.134)于6天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Docker。

有什么可以替代它?

同一分类中有:GitHub, Desktop Commander, Cloudflare MCP

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

GitHub

作者:github

Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.

电脑或云端★ 34,245MIT5天前更新
安装

Desktop Commander

作者:wonderwhy-er

MCP server for terminal commands, file operations, and process management

在你的电脑上运行★ 10,225MIT17天前更新
安装

Cloudflare MCP

作者:cloudflare

Cloudflare MCP servers

在云端运行★ 4,446Apache-2.014个月前更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

🛡️ Agent Workspace MCP Server

A unified Model Context Protocol (MCP) server providing a highly secure, containerized workspace for Large Language Models (LLMs). It acts as an isolated "agentic playground" where agents can autonomously code, test, and debug without risking the host machine.


✨ Features

  • 🏗️ Full Project Lifecycle: Bootstrap projects with uv init, manage dependencies with uv add, and execute via uv run.
  • 🐚 Secure Bash Access: Execute shell commands with mandatory timeouts and merged output streams.
  • 🚀 Token-Optimized Output: Integrates RTK (Rust Token Killer) to automatically filter and compress run_bash outputs (like ls, git, and test runners), saving 60-90% of LLM context tokens.
  • 📂 Robust Filesystem: Path-traversal protected operations for reading, writing, and searching the workspace.
  • 🛡️ Multi-Layer Security: Non-root execution, dropped capabilities, resource limits, and a read-only root filesystem.
  • ⚡ Precision Editing: Advanced search_and_replace with fuzzy whitespace matching, indentation preservation, dry-run support, and syntax validation for Python, JSON, JSONL, TOML, and YAML.
  • 📊 Real-time Observability: Direct logging to MCP client UI and persistent rotating audit logs.

🏗️ Architecture

flowchart TD

Client["MCP Client (Claude / Cursor)"] -- "stdio (JSON-RPC)" --> FastMCP["FastMCP Server"]

subgraph Sandbox ["Docker Sandbox Container (mcpuser)"] direction TB

FastMCP -. "Intercepts accidental prints" .-> StdioGuard["StdoutRedirector"] FastMCP -. "Application Logs" .-> Logger["Dual Logger (stderr & .mcp/server.log)"]

FastMCP -- "Tool Calls" --> SecurityGuard["Security & Path Validator"]

subgraph Toolset ["Tool Modules"] direction TB SecurityGuard --> FSTools["Filesystem (read, write, list, search)"] SecurityGuard --> EditTools["Editing (search_and_replace)"] SecurityGuard --> ExecTools["Execution (run_bash)"] end

EditTools -- "AST Verification" --> Validator["Syntax Validations (Python, JSON, JSONL, TOML, YAML)"] ExecTools -- "Process Group (Timeout=60s)" --> Shell["/bin/sh Subprocess"] Shell -- "Package Mgt & Checks" --> UV["uv Environment / Ruff"]

FSTools -- "Secure I/O" --> Workspace["/workspace Directory"] EditTools -- "Atomic Writes" --> Workspace Shell -- "Executes within" --> Workspace end

Workspace <--"Volume Mount"--> HostFS["User Host Filesystem"]


📦 Quick Start

1. Pull or Build the Docker Image
# Pull from GHCR

docker pull ghcr.io/hrrodan/agent-workspace-mcp:latest

OR: Build locally with your host's UID/GID for optimal permissions

docker build --build-arg UID=$(id -u) --build-arg GID=$(id -g) -t agent-workspace-mcp .

2. Programmatic Usage (OpenAI Agents SDK)

Here is a quick boilerplate showing how to use the containerized workspace programmatically using the standard openai-agents SDK:

import asyncio

from agents import Agent, Runner from agents.mcp import MCPServerStdio

async def main():

1. Configure the MCP Server to run via Docker

server = MCPServerStdio( name="Sandboxed Workspace", params={ "command": "docker", "args": [ "run", "-i", "--rm", "--init",

"--network", "none", # Network Isolation (optional) - see below

"--memory=2g", "--cpus=2.0", "--pids-limit=256", "--cap-drop=ALL", "--security-opt=no-new-privileges:true", "--read-only", "--tmpfs", "/tmp:size=64m", "--tmpfs", "/home/mcpuser/.cache:size=512m", "--user", "1000:1000", # Replace with your host UID:GID "-v", "/path/to/your/projects:/workspace", "ghcr.io/hrrodan/agent-workspace-mcp:latest", ], }, client_session_timeout_seconds=60.0, )

2. Attach server to the Agent and load the skill instructions (optional)

with open("skills/agent-workspace-mcp/SKILL.md", "r") as f: skill_instructions = f.read()

agent = Agent( name="WorkspaceAgent", instructions=f"You are a coding agent with access to a secure workspace.\n\n{skill_instructions}", mcp_servers=[server], )

3. Execute a workflow

async with server: result = await Runner.run( agent, "Create a python script in the workspace to print the first 10 Fibonacci numbers, then run it." ) print(f"Agent's Final Output:\n{result.final_output}")

if name == "main": asyncio.run(main())

3. Use with MCP Clients (Claude / Cursor)

Add the following configuration to your claude_desktop_config.json or Cursor settings.

{

"mcpServers": { "agent-workspace-mcp": { "command": "docker", "args": [ "run", "-i", "--rm", "--init", // "--network", "none", // Network Isolation (optional) - see below "--memory=2g", "--cpus=2.0", "--pids-limit=256", "--cap-drop=ALL", "--security-opt=no-new-privileges:true", "--read-only", "--tmpfs", "/tmp:size=64m", "--tmpfs", "/home/mcpuser/.cache:size=512m", "--user", "1000:1000", "-v", "/path/to/your/projects:/workspace", "ghcr.io/hrrodan/agent-workspace-mcp:latest" ] } } }

[!IMPORTANT]

Linux Users: Replace 1000:1000 with your actual UID:GID (run id -u and id -g). Claude Desktop does not expand environment variables.

Signal Handling: The --init flag is essential for proper signal forwarding and zombie process reaping.


🛠️ Tool Reference

ToolDescription
read_fileRead text files with optional offset and limit (default: 100 lines).
write_fileCreate files with syntax validation and a 5MB size guard. Refuses to overwrite existing files by default (create_only=True).
list_directoryList contents with [F]ile and [D]irectory prefixes.
search_workspaceFind files by glob pattern with support for exclude_patterns.
run_bashExecute shell commands in /workspace with a 60s timeout. Automatically optimized via RTK to reduce token usage.
search_and_replaceMulti-edit tool with fuzzy whitespace matching, indentation preservation, dry-run mode, and syntax validation (Python, JSON, JSONL, TOML, YAML).

⚙️ Configuration

The server supports the following environment variables (passed via Docker --env):

VariableDefaultDescription
COMMAND_TIMEOUT60Default seconds before run_bash kills a process.
MAX_SEARCH_RESULTS50Maximum results returned by search_workspace.
MAX_READ_SIZE_BYTES1048576Maximum file size for read_file (1MB).
MAX_WRITE_SIZE_BYTES5242880Maximum file size for write_file (5MB).
LOG_LEVELINFOPython logging level (DEBUG, INFO, etc.).

🛡️ Security & Architecture Model

This server employs a defense-in-depth strategy, explicitly separating strict security boundaries from developer experience and operational reliability features.

🔒 Core Security Features

These features are designed to protect the host system and enforce strict isolation boundaries.

  • Kernel Hardening: All Linux capabilities are dropped (--cap-drop=ALL), neutralizing privilege escalation vectors.
  • Immutable Server Code: The /app directory containing the server source and its virtual environment is owned by root and read-only for the mcpuser. This prevents the server from modifying itself or being tampered with via run_bash.
  • Privilege Lockdown: Enforces no-new-privileges:true to prevent any process from gaining elevated rights.
  • Immutable System Core: The container's root filesystem is mounted entirely read-only, providing a second layer of defense against OS-level tampering.
  • Resource Quotas: Hard limitations on CPU, Memory, and PIDs mitigate denial-of-service (DoS) attempts like fork-bombs and host exhaustion.
  • Strict Boundary Enforcement: A robust path validator comprehensively blocks all path traversal attacks outside the designated /workspace.
  • Process & Resource Control: Mandatory command timeouts (default 60s) and strict process group isolation ensure runaway or malicious processes are killed.
  • Memory-Overload Protection: Hard limits on file reads (1MB) and command outputs (50KB) prevent memory exhaustion.
  • Information Leakage Prevention: Internal stack traces and system paths are suppressed and sanitized from tool outputs.
🛠️ Developer Experience & Convenience

Features focused on seamless integration, usability, and reducing friction during agentic workflows.

  • Host-Aligned Non-Root Identity: Runs as mcpuser with UID/GID customizable at build time, eliminating tedious file permission conflicts on host volume mounts.
  • Automatic Token Optimization: Shell commands executed via run_bash are transparently rewritten through RTK to provide ultra-compact, LLM-friendly output without altering underlying command behavior.
  • Intelligent Search Exclusions: High-noise or sensitive directories (.git, .venv) are automatically ignored to keep context windows lean and relevant.
  • Ephemeral Workspaces: Containers are strictly ephemeral (--rm), guaranteeing a clean, predictable slate for every new session without state leaking across connections.
  • Standardized Discovery: Complies with the OCI Image Specification for standardized container ecosystem integration and transparent auditing.
⚙️ Reliability & Safety Mechanisms

Features ensuring the structural integrity of the workspace and providing observability.

  • Pre-Write Syntax Validation: Both write_file and search_and_replace perform in-memory syntax validation for Python, JSON, JSONL, TOML, and YAML before persisting changes, preventing broken code states.
  • Fail-Safe Writing: write_file blocks accidental overwrites of existing files by default and enforces a 5MB size guard to prevent workspace flooding.
  • Atomic File Operations: Edits utilize temp-and-move logic to guarantee file integrity and prevent corruption, even during unexpected interruptions or crashes.
  • Transparent Observability: All tool invocations and state changes are streamed in real-time to the MCP client UI for immediate operator oversight.
🌐 Network Isolation (Optional)

By default, the container has full network access via Docker's bridge network. For maximum isolation, you can completely disable the network stack using --network none:

docker run -i --rm --init \

--network none \ --memory=2g --cpus=2.0 --pids-limit=256 \ --cap-drop=ALL --security-opt=no-new-privileges:true \ --read-only \ --tmpfs /tmp:size=64m \ --tmpfs /home/mcpuser/.cache:size=512m \ --user 1000:1000 \ -v /path/to/your/projects:/workspace \ ghcr.io/hrrodan/agent-workspace-mcp:latest

This creates a fully air-gapped sandbox — only the loopback interface exists inside the container. All outbound connections (curl, DNS, uv add, etc.) will fail immediately, eliminating data exfiltration and lateral m

只显示了开头部分;其余内容请在仓库中阅读

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