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Agent Workspace MCP

by HrRodanversion 1.4.134i★ –iRuns on your computer
Registry nameio.github.HrRodan/agent-workspace-mcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account HrRodan, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

  • More stars than 0% of the other 4921 servers in this category
  • Published to the registry more recently than 85% of the other 4921 servers in this category
  • Basic checks equal to or better than 100% of the other 4921 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Agent Workspace MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.4.134) reached the registry 6 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

GitHub

by github

GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.

Computer or cloud★ 34,245MITUpdated 4 days ago
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Desktop Commander

by wonderwhy-er

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

Runs on your computer★ 10,225MITUpdated 17 days ago
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Cloudflare MCP

by cloudflare

Cloudflare MCP servers

Runs in the cloud★ 4,446Apache-2.0Updated 14 months ago
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

🛡️ 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

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