cargoshipper-mcp

cargoshipper-mcp

A comprehensive MCP server that provides Claude with direct access to Docker, DigitalOcean, and CloudFlare APIs for infrastructure management and automation.

Category
访问服务器

README

CargoShipper MCP Server

Python 3.11+ MCP PyPI uvx

A comprehensive MCP (Model Context Protocol) server that provides Claude with direct access to Docker, DigitalOcean, and CloudFlare APIs for infrastructure management and automation.

⚠️ IMPORTANT SAFETY WARNING

USE AT YOUR OWN RISK: This tool gives AI models direct access to infrastructure management APIs that can:

  • Create, modify, or delete cloud resources (potentially incurring costs)
  • Modify DNS settings (potentially breaking domains)
  • Manage Docker containers and images (potentially affecting running services)
  • Execute destructive operations on your infrastructure

Before using:

  • Ensure you understand the capabilities and risks of each API
  • Start with non-production environments only
  • Review all operations before execution in production
  • Monitor costs and resource usage carefully
  • Have backup and recovery procedures in place

AI models may:

  • Misinterpret instructions and perform unintended operations
  • Make mistakes in resource configuration or deletion
  • Execute multiple operations when only one was intended
  • Struggle with complex multi-step procedures requiring human judgment

Recommended safety measures:

  • Use dedicated development/testing accounts with spending limits
  • Implement least-privilege API tokens with restricted permissions
  • Monitor all operations through cloud provider dashboards
  • Test operations in isolated environments first
  • Keep backups of critical configurations and data

🤖 VIBECODE FRIENDLY PROJECT

This project welcomes contributions from AI/LLM agents! Pull requests from Claude, GPT, and other AI models are actively encouraged.

Guidelines for AI contributors:

  • Follow existing code patterns and documentation standards
  • Include comprehensive commit messages explaining changes
  • Test changes thoroughly before submitting PRs
  • Update documentation when adding new features

✨ Easy Setup with uvx

CargoShipper is available on PyPI and works seamlessly with uvx for easy installation and management, just like mcp-server-git.

Quick Install from PyPI

# Run directly (recommended)
uvx cargoshipper-mcp

# Configure in your .mcp.json
{
  "mcpServers": {
    "cargoshipper": {
      "command": "uvx",
      "args": ["cargoshipper-mcp"]
    }
  }
}

Development Setup

For local development and testing:

  1. Clone the repository:

    cd cargoshipper-mcp
    
  2. Install dependencies:

    pip install -r requirements.txt
    # or create a virtual environment first
    python3 -m venv .venv
    source .venv/bin/activate  # Linux/Mac
    pip install -r requirements.txt
    
  3. Configure your APIs:

    cp .env.example .env
    # Edit .env with your API tokens:
    # DIGITALOCEAN_TOKEN=your_token_here
    # CLOUDFLARE_API_TOKEN=your_token_here
    
  4. Use the development MCP configuration:

    # Use .mcp.dev.json for local development
    cp .mcp.dev.json .mcp.json
    

🐳 Docker Integration

  • Container Management: Full lifecycle (create, start, stop, remove, logs)
  • Image Operations: List, pull, and manage Docker images
  • System Information: Docker system stats and health
  • Resource Monitoring: Container resource usage and status

🌊 DigitalOcean Integration

  • Droplet Management: Complete droplet lifecycle management
  • DNS Management: Full DNS record CRUD operations
  • Account Information: Access account details and billing
  • Image Management: Work with distributions and custom snapshots

☁️ CloudFlare Integration

  • Zone Management: Create and configure CloudFlare zones
  • DNS Operations: Advanced DNS with proxy settings
  • Cache Control: Purge cache by URL, tags, or everything
  • Analytics: Traffic and performance analytics
  • Security Settings: SSL, security levels, firewall rules

🔧 Available Tools & Resources

30 Tools Total:

  • Docker (9 tools): docker_run_container, docker_list_containers, etc.
  • DigitalOcean (10 tools): do_create_droplet, do_list_dns_records, etc.
  • CloudFlare (11 tools): cf_create_zone, cf_purge_cache, etc.

17 Resources Total:

  • docker://containers - All containers with status
  • digitalocean://droplets - All droplets with costs
  • cloudflare://zones - All zones with analytics
  • And many more...

📋 Configuration Files

Production (.mcp.json)

{
  "mcpServers": {
    "cargoshipper": {
      "command": "uvx", 
      "args": ["cargoshipper-mcp"]
    }
  }
}

Development (.mcp.dev.json)

{
  "mcpServers": {
    "cargoshipper": {
      "command": "python",
      "args": ["-m", "cargoshipper_mcp.server"],
      "cwd": ".",
      "env": {
        "PYTHONPATH": "."
      }
    }
  }
}

🔑 API Credentials Setup

Create .env file or ~/.config/cargoshipper-mcp/.env:

# DigitalOcean API Token
DIGITALOCEAN_TOKEN=your_digitalocean_token_here

# CloudFlare API Token (recommended)
CLOUDFLARE_API_TOKEN=your_cloudflare_token_here

# Alternative: CloudFlare Email + Global API Key
# CLOUDFLARE_EMAIL=your@email.com
# CLOUDFLARE_API_KEY=your_global_api_key

Getting API Tokens:

🚀 Usage Examples

Once configured, Claude will have access to infrastructure operations:

# Docker examples
"Run an nginx container on port 8080"
"List all running containers"
"Get logs from container abc123"

# DigitalOcean examples  
"Create a small droplet in NYC3"
"List all my droplets and their costs"
"Add an A record for api.example.com"

# CloudFlare examples
"Create a new zone for mysite.com"
"Purge all cache for example.com" 
"Show me analytics for the last 24 hours"

📁 Project Structure

cargoshipper-mcp/
├── cargoshipper_mcp/        # Main package (renamed from src/)
│   ├── server.py           # MCP server entry point  
│   ├── config/             # Configuration with multi-path .env loading
│   ├── tools/              # API operation tools
│   │   ├── docker.py       # Docker operations
│   │   ├── digitalocean.py # DigitalOcean operations
│   │   └── cloudflare.py   # CloudFlare operations
│   ├── resources/          # Read-only data access
│   └── utils/              # Shared utilities
├── .mcp.json              # Production MCP config (uvx)
├── .mcp.dev.json          # Development MCP config (local python)
├── pyproject.toml         # Python packaging (uvx compatible)
├── requirements.txt       # Dependencies
└── install.sh             # uvx installation script

🌍 Published on PyPI

CargoShipper MCP is now available on PyPI! Access it at: https://pypi.org/project/cargoshipper-mcp/

Setup is as simple as:

# Run directly (most common)
uvx cargoshipper-mcp

# Configure in .mcp.json
{
  "mcpServers": {
    "cargoshipper": {
      "command": "uvx",
      "args": ["cargoshipper-mcp"] 
    }
  }
}

🛠️ Development

Package Structure

  • Uses proper Python packaging with pyproject.toml
  • Console entry point: cargoshipper-mcp = "cargoshipper_mcp.server:main"
  • Multi-path environment loading for uvx compatibility
  • Type hints and comprehensive error handling throughout

Testing

python test_server.py  # Validates imports and configuration

This approach follows the same pattern as mcp-server-git and other uvx-compatible MCP servers, making it extremely easy to install and use once published!

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选