Agent Workspace MCP Server
# 🛡️ Agent Workspace MCP Server
[](https://github.com/HrRodan/agent-workspace-mcp/actions/workflows/ci.yml)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/release/python-3140/)
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)](https://github.com/rtk-ai/rtk) 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
```mermaid
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
```bash
# 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:
```python
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.
```json
{
"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
| 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)**. |
---
## ⚙️ Configuration
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.). |
---
## 🛡️ 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](#1-pull-or-build-the-docker-image), 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](https://github.com/opencontainers/image-spec) 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`:
```bash
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 movement risks entirely.
> [!NOTE]
> With `--network none`, the agent cannot install packages at runtime. All dependencies must be pre-installed in a custom image or pre-populated in the mounted workspace volume.
---
## 🤝 Contributing
1. **Install Dev Dependencies**: `uv sync`
2. **Run Linting**: `uv run ruff check .`
3. **Run Unit Tests**: `uv run pytest tests/ --ignore=tests/integration/`
4. **Run Integration Tests**: Set `OPENROUTER_API_KEY` and run `uv run pytest tests/integration/`
---
© 2026 HrRodan. Licensed under [MIT](LICENSE).
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose: read_file for reading, write_file for overwriting, list_directory for listing, search_workspace for locating files by pattern, run_bash for arbitrary shell commands, and search_and_replace for partial edits. No ambiguity.
All tool names follow a consistent snake_case verb_noun pattern (e.g., read_file, write_file, list_directory). The only exception is search_and_replace, which combines two verbs, but it still aligns with the overall style.
With 6 tools, the set is well-scoped for an agent workspace. It covers essential operations (file read/write, directory listing, file search, shell execution, and text editing) without unnecessary clutter.
The tool set covers most common operations, but lacks dedicated tools for file deletion, renaming, or appending. However, these gaps can be addressed via run_bash, so only minor incompleteness is present.