Dummy MCP Server (Contabo + Hetzner)
A demonstration MCP server that simulates server management actions for Contabo and Hetzner using FastAPI. It provides a mock environment for testing request routing and provider-based service modules without requiring live API credentials.
README
Dummy MCP Server (Contabo + Hetzner)
This project is a dummy MCP server built for interview/demo purposes.
It simulates the behavior of an MCP (Model Control Plane) server that connects to service providers like Contabo and Hetzner, but without calling real APIs.
Why dummy Service providers?
Since, in order to utilize Contabo and Hetzner services, we have to go through payment and billing. Therefore, to understand the basic working of MCP with service providers, we created fake instances!
Purpose
The goal of this project is to demonstrate:
- Clean architecture
- Request routing logic
- Provider-based service modules
- Working client-server flow
- Frontend dialog response display
- Interview-ready demo
Features
✔ Dummy MCP server with clean request routing
✔ Two service providers (Contabo & Hetzner)
✔ Dummy “create_server” & “delete_server” actions
✔ Frontend UI with dialog box to show response
✔ CORS enabled for browser requests
✔ Easy to extend (add real APIs later)
Architectural Diagram
┌─────────────────────┐
│ MCP Client │
│ (Frontend / CLI) │
└─────────┬───────────┘
│ POST /mcp
│
┌──────────▼───────────┐
│ MCP Server │
│ (FastAPI) │
└──────────┬───────────┘
│
┌───────────────┴───────────────┐
│ │
┌───────▼────────┐ ┌────────▼───────┐
│ ContaboService │ │ HetznerService │
│ (Dummy Logic) │ │ (Dummy Logic) │
└────────────────┘ └────────────────┘
Step-by-Step Setup (Commands Used)
1. Create project folder
mkdir mcp-server
cd mcp-server
2. Initialize Git
git init
3. Create Python Virtual Environment
python3 -m venv venv
source venv/bin/activate
4. Install dependencies
pip install fastapi uvicorn
5. Save dependencies
pip freeze > requirements.txt
6. Create folders & files
mkdir services
touch main.py
touch services/contabo.py
touch services/hetzner.py
touch index.html
touch README.md
Running the Server
uvicorn main:app --reload --port 3000
You should see:
INFO: Uvicorn running on http://127.0.0.1:3000
Testing via Curl (Contabo)
curl -X POST http://localhost:3000/mcp \
-H "Content-Type: application/json" \
-d '{
"provider": "contabo",
"action": "create_server",
"payload": {"name": "test-server", "ram": "4GB"}
}'
Testing via Curl (Hetzner)
curl -X POST http://localhost:3000/mcp \
-H "Content-Type: application/json" \
-d '{
"provider": "hetzner",
"action": "create_server",
"payload": {"name": "hetzner-server", "ram": "8GB"}
}'
Frontend Demo
Open index.html in your browser and:
- Select Provider
- Select Action
- Enter Payload
- Click Send Request
- Response appears in a dialog box
Logic Explanation
1. MCP Server (main.py)
- Receives POST request at
/mcp - Checks provider name
- Routes request to respective service module
2. Contabo Service (services/contabo.py)
- Simulates server creation & deletion
- Returns fake server ID
3. Hetzner Service (services/hetzner.py)
- Same dummy logic as Contabo
GitHub Push Commands
git add .
git commit -m "Initial dummy MCP server implementation"
git branch -M main
git remote add origin https://github.com/<your-username>/mcp-server.git
git push -u origin main
推荐服务器
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。
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。
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。
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。
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。
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。
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。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
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。