GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
作者提供的说明(英文原文)
A sandboxed, agentic workspace providing secure filesystem, bash, and uv-powered Python execution.
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。
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。
选择你使用的 AI 应用,然后复制设置。
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。
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。
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
注册名称已通过 GitHub 账户 HrRodan 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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。
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。
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)。
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。
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。
同一分类中有:GitHub, Desktop Commander, Cloudflare MCP
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
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
Cloudflare MCP servers
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。
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.
uv init, manage dependencies with uv add, and execute via uv run.run_bash outputs (like ls, git, and test runners), saving 60-90% of LLM context tokens.search_and_replace with fuzzy whitespace matching, indentation preservation, dry-run support, and syntax validation for Python, JSON, JSONL, TOML, and YAML.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"]
# Pull from GHCR
docker pull ghcr.io/hrrodan/agent-workspace-mcp:latest
docker build --build-arg UID=$(id -u) --build-arg GID=$(id -g) -t agent-workspace-mcp .
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():
server = MCPServerStdio( name="Sandboxed Workspace", params={ "command": "docker", "args": [ "run", "-i", "--rm", "--init",
"--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, )
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], )
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())
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
1000:1000with your actual UID:GID (runid -uandid -g). Claude Desktop does not expand environment variables.
Signal Handling: The
--initflag is essential for proper signal forwarding and zombie process reaping.
| Tool | Description |
|---|---|
read_file | Read text files with optional offset and limit (default: 100 lines). |
write_file | Create files with syntax validation and a 5MB size guard. Refuses to overwrite existing files by default (create_only=True). |
list_directory | List contents with [F]ile and [D]irectory prefixes. |
search_workspace | Find files by glob pattern with support for exclude_patterns. |
run_bash | Execute shell commands in /workspace with a 60s timeout. Automatically optimized via RTK to reduce token usage. |
search_and_replace | Multi-edit tool with fuzzy whitespace matching, indentation preservation, dry-run mode, and syntax validation (Python, JSON, JSONL, TOML, YAML). |
The server supports the following environment variables (passed via Docker --env):
| Variable | Default | Description |
|---|---|---|
COMMAND_TIMEOUT | 60 | Default seconds before run_bash kills a process. |
MAX_SEARCH_RESULTS | 50 | Maximum results returned by search_workspace. |
MAX_READ_SIZE_BYTES | 1048576 | Maximum file size for read_file (1MB). |
MAX_WRITE_SIZE_BYTES | 5242880 | Maximum file size for write_file (5MB). |
LOG_LEVEL | INFO | Python logging level (DEBUG, INFO, etc.). |
This server employs a defense-in-depth strategy, explicitly separating strict security boundaries from developer experience and operational reliability features.
These features are designed to protect the host system and enforce strict isolation boundaries.
--cap-drop=ALL), neutralizing privilege escalation vectors./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.no-new-privileges:true to prevent any process from gaining elevated rights./workspace.Features focused on seamless integration, usability, and reducing friction during agentic workflows.
mcpuser with UID/GID customizable at build time, eliminating tedious file permission conflicts on host volume mounts.run_bash are transparently rewritten through RTK to provide ultra-compact, LLM-friendly output without altering underlying command behavior..git, .venv) are automatically ignored to keep context windows lean and relevant.--rm), guaranteeing a clean, predictable slate for every new session without state leaking across connections.Features ensuring the structural integrity of the workspace and providing observability.
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.write_file blocks accidental overwrites of existing files by default and enforces a 5MB size guard to prevent workspace flooding.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