Docker MCP Server
Enables executing code in isolated Docker containers with strict resource limits, session management, container reuse, and automatic dependency installation.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Docker MCP Server
A Model Context Protocol (MCP) server that enables LLMs to safely execute code in isolated Docker containers with strict resource limits and security controls.
Features
š Secure Isolation: Containers run with strict resource limits (memory, CPU, PIDs)
š·ļø Session Management: Group containers by session with persistent workspaces
ā»ļø Container Reuse: Optimize performance by reusing existing containers
š¦ Smart Dependencies: Auto-detect and install packages (pip, npm, apt, apk)
š Streaming Output: Real-time output for long-running processes
š¾ Persistent Workspaces: Session-based volumes maintain state across executions
Related MCP server: Advanced MCP Server
Installation
# Clone the repository
git clone https://github.com/cevatkerim/docker-mcp.git
cd docker-mcp
# Install in development mode
pip install -e .
# Install development dependencies
pip install -r requirements-dev.txtPrerequisites
Python 3.10+
Docker Engine running locally
MCP-compatible client (e.g., Claude Desktop)
Quick Start
1. Start the MCP Server
python -m docker_mcp2. Configure Your MCP Client
Add to your MCP client configuration:
{
"mcpServers": {
"docker": {
"command": "python",
"args": ["-m", "docker_mcp"]
}
}
}Available Tools
1. check_engine
Check Docker engine availability and version.
result = check_engine()
# Returns: Docker version and status2. list_containers
List Docker containers with optional filtering.
result = list_containers(
show_all=True, # Show all containers, not just running
session_id="my-session" # Filter by session
)3. create_container
Create and start a new container with resource limits.
result = create_container(
image="python:3.11-slim",
name="my-container",
session_id="my-session",
network_enabled=False, # Network isolation by default
reuse_existing=True, # Reuse if exists
environment={"KEY": "value"}
)4. execute_code
Execute commands in a container.
result = execute_code(
container_id="my-container",
command="echo 'Hello, World!'",
timeout=30,
stream=True, # Stream output in real-time
working_dir="/workspace"
)5. execute_python_script
Execute Python scripts with automatic dependency management.
result = execute_python_script(
container_id="my-container",
script="import numpy; print(numpy.__version__)",
packages=["numpy"], # Auto-install if needed
timeout=60
)6. add_dependencies
Install packages in a running container.
result = add_dependencies(
container_id="my-container",
packages=["requests", "pandas"],
package_manager="pip" # Auto-detected if not specified
)7. cleanup_container
Stop and remove containers with optional volume cleanup.
# Remove specific container
result = cleanup_container(container_id="my-container")
# Remove all containers for a session
result = cleanup_container(session_id="my-session", remove_volumes=True)
# Remove all MCP-managed containers
result = cleanup_container(cleanup_all=True)Security Features
Resource Limits
Memory: 1GB default (configurable)
CPU: 1.0 cores default (configurable)
Process IDs: 512 max (configurable)
Network: Isolated by default, opt-in for network access
Container Labels
All containers are labeled with mcp-managed=true for easy identification and cleanup.
Workspace Isolation
Each container gets a /workspace directory backed by a named volume, preventing host filesystem access.
Configuration
Configure via environment variables:
export DOCKER_MCP_MEMORY_LIMIT=2147483648 # 2GB in bytes
export DOCKER_MCP_CPU_LIMIT=2.0 # 2 CPU cores
export DOCKER_MCP_PIDS_LIMIT=1024 # Max processes
export DOCKER_MCP_TIMEOUT=60 # Default timeout
export DOCKER_MCP_DEBUG=true # Enable debug loggingExamples
Example 1: Python Data Analysis
# Create a container for data analysis
container = create_container(
image="python:3.11-slim",
session_id="data-analysis"
)
# Install required packages
add_dependencies(
container_id=container['container_id'],
packages=["pandas", "matplotlib", "seaborn"]
)
# Execute analysis script
script = """
import pandas as pd
import matplotlib.pyplot as plt
# Create sample data
df = pd.DataFrame({
'x': range(10),
'y': [i**2 for i in range(10)]
})
# Save plot
df.plot(x='x', y='y')
plt.savefig('/workspace/plot.png')
print("Plot saved to /workspace/plot.png")
print(df.describe())
"""
execute_python_script(
container_id=container['container_id'],
script=script
)Example 2: Node.js Development
# Create Node.js container
container = create_container(
image="node:18-alpine",
session_id="nodejs-dev",
network_enabled=True # Need network for npm
)
# Install packages
add_dependencies(
container_id=container['container_id'],
packages=["express", "axios"],
package_manager="npm"
)
# Run Node.js code
execute_code(
container_id=container['container_id'],
command="node -e \"console.log('Node version:', process.version)\""
)Example 3: Multi-Language Project
# Create container with Python and Node.js
container = create_container(
image="nikolaik/python-nodejs:python3.11-nodejs18",
session_id="multi-lang"
)
# Install Python packages
add_dependencies(
container_id=container['container_id'],
packages=["fastapi", "uvicorn"],
package_manager="pip"
)
# Install Node packages
add_dependencies(
container_id=container['container_id'],
packages=["webpack", "babel-core"],
package_manager="npm"
)Development
Running Tests
# Run all tests
pytest
# Run with coverage
pytest --cov=src --cov-report=term
# Run specific test file
pytest tests/test_docker_client.py -vProject Structure
docker-mcp/
āāā src/
ā āāā docker_mcp/
ā āāā __init__.py
ā āāā server.py # MCP server implementation
ā āāā container_ops.py # Tool implementations
ā āāā docker_client.py # Docker SDK wrapper
ā āāā config.py # Configuration
ā āāā schemas.py # Data models
āāā tests/
ā āāā test_docker_client.py
ā āāā test_tools_comprehensive.py
ā āāā ...
āāā pyproject.toml
āāā requirements.txt
āāā README.mdTroubleshooting
Docker Not Available
Error: Cannot connect to Docker daemonSolution: Ensure Docker Desktop is running and the Docker socket is accessible.
Permission Denied
Error: Permission denied while trying to connect to Docker daemonSolution: Add your user to the docker group or run with appropriate permissions.
Container Creation Failed
Error: Image not foundSolution: The image will be automatically pulled. Ensure you have internet connectivity.
Contributing
Fork the repository
Create a feature branch
Write tests for new functionality
Implement the feature
Ensure all tests pass
Submit a pull request
License
MIT License - see LICENSE file for details.
Support
For issues and questions, please open an issue on GitHub.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelogā¦
This server cannot be deployed
Maintenance
Related MCP Connectors
Build Apps and run code in 30 languages ā sandboxed, with persistent sessions for agent loops.
Cloud Linux VMs for your agent: run code, Docker, files and HTTPS previews in a Sandboxer microVM.
Operate Linux, macOS and Windows from your LLM. Every action runs through an auditable allowlist.
Run Python code in a secure sandbox without local setup. Declare inline dependencies and execute sā¦
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceAn interactive Python code execution environment that allows users and LLMs to safely execute Python code and install packages in isolated Docker containers.39Apache 2.0
- AlicenseNot gradedqualityNot gradedmaintenanceProvides AI coding agents with a secure, sandboxed environment for executing coding tasks including file operations, command execution, and testing. Features session management, policy enforcement, and Docker-based sandboxing for safe code execution and development workflows.MIT
- AlicenseNot gradedqualityBmaintenanceEnables LLMs to execute Python code securely in a sandboxed environment. Supports configurable restrictions like no network access and returns results including files.MIT
- AlicenseAqualityCmaintenanceEnables AI assistants to securely execute Python and C++ code snippets inside disposable, isolated Docker containers and receive structured results.31MIT