DockaShell
About
DockaShell is an MCP server that gives AI agents isolated Docker containers to work in. MCP tools for shell access, file operations, and full audit trail.
Details
- Author
- anzax
- GitHub stars
- 29
- Downloads
- 368
- Categories
- Other, Developer Tools
Jump to
- Shell-based workflows instead of specialized tools
- Container isolation for host system safety
- Persistent workspace surviving across sessions
- Complete audit trail for every command
- Self-evolving agents building their own tools
- Autonomous exploration without constant approval
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
DockaShellCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install globally with npm install -g dockashell, then run dockashell build, dockashell create my-project, and dockashell start my-project. Add the server to your MCP client configuration using command dockashell with args ["serve"]. Requires Node.js 20+ and Docker running.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"dockashell": {
"dockashell": {
"command": "dockashell",
"args": [
"serve"
]
}
}
}
}McpServers
{
"dockashell": {
"command": "dockashell",
"args": [
"serve"
]
}
}DockaShell
DockaShell is an MCP (Model Context Protocol) server that gives AI agents isolated Docker containers to work in. Each agent gets its own persistent environment with shell access, file operations, and full audit trails.
> This is a research project exploring agent autonomy: How far can we push shell-based workflows? Can agents manage their own development environments and create their own tools?
Why this exists
Current AI assistants hit fundamental walls:
- No persistent memory: Conversations reset, context is lost, agents can't build on previous work
- Tool babysitting: Every shell command needs human approval, breaking agent flow and autonomy
- Limited toolsets: Agents stuck with predefined tools instead of building what they need
- No self-reflection: Can't analyze their own traces to improve or learn from past sessions
DockaShell removes these constraints to explore what emerges:
- Self-evolving agents: Build and refine their own tools, scripts, and workflows
- Continuous memory: Maintain knowledge bases, wikis, notebooks that persist across sessions
- Autonomous exploration: Run shell commands without constant human intervention
- Meta-learning: Analyze previous traces to improve decision-making and tool usage
The core question: What can agents accomplish when they have real persistence and autonomy?
How it works
AI Agent (Claude/GPT/...)
↔ DockaShell (MCP Server)
└─ Docker Engine
├─ Container A (Project 1)
│ └─ Persistent Volume
├─ Container B (Project 2)
│ └─ Persistent Volume
└─ Container C (Project 3)
└─ Persistent Volume
Each AI agent gets its own isolated Docker container with persistent storage. Instead of dozens of custom tools, agents use standard shell commands (bash, git, npm, etc.) and build their own workflows.
Key principles:
- Shell > specialized tools: Agents already "speak" POSIX, so let them use real commands
- Container isolation: Full autonomy inside, zero risk to your host system
- Persistent workspace: Files, databases, and context survive across sessions
- Complete audit trail: Every command and file change is logged for analysis
→ See detailed architecture and security model
Quick Start
```bash
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