Demo MCP Server
Minimal FastMCP Python server demonstrating basic MCP functionality with a cowsay tool for generating ASCII art.
README
Demo MCP Server
A minimal FastMCP Python server demonstrating basic MCP (Model Context Protocol) functionality with a simple cowsay tool.
Features
- FastMCP Server: Simple MCP server implementation
- Cowsay Tool: Generate ASCII art of a cow saying custom messages
- Environment Configuration: .env file support with Pydantic settings
- Docker Support: Full Docker and docker-compose setup
- Testing: Basic unit tests with pytest
- KISS Principle: Keep It Simple, focused implementation
Quick Start
Local Development
-
Install dependencies:
make install-dev -
Run the server (http mode on port 8005):
make run-http -
Run tests:
make test
Docker
-
Build and run:
make docker-build make docker-run -
For HTTP transport:
make docker-run-http
Project Structure
my-mcp-server/
├── src/
│ ├── config/
│ │ └── settings.py # Environment configuration
│ └── mcp/
│ ├── __main__.py # Entry point
│ ├── server.py # FastMCP server setup
│ └── tools/
│ └── cowsay.py # Cowsay MCP tool
├── tests/ # Unit tests
├── .env.example # Environment variables template
├── docker-compose.yml # Docker compose configuration
├── Dockerfile # Docker container definition
├── Makefile # Common commands
└── pyproject.toml # Python project configuration
Available Tools
cowsay
Generate ASCII art of a cow saying a message.
Parameters:
message(str): The message for the cow to saycow(str, optional): The type of cow to use (default: "default")
Example:
result = await cowsay("Hello, World!")
Output:
_______________
< Hello, World! >
---------------
\ ^__^
\ (oo)\_______
(__)\ )\/\
||----w |
|| ||
Configuration
The server uses environment variables for configuration. Copy .env.example to .env and customize:
cp .env.example .env
Available Settings
| Variable | Default | Description |
|---|---|---|
APP_NAME |
"Demo MCP Server" | Application name |
LOG_LEVEL |
"INFO" | Logging level |
MCP_TRANSPORT |
"stdio" | MCP transport method |
MCP_HOST |
"127.0.0.1" | Host for HTTP transport |
MCP_PORT |
"8000" | Port for HTTP transport |
MAX_MESSAGE_LENGTH |
"500" | Maximum cowsay message length |
Usage with MCP Clients
Claude Desktop
Add to your MCP settings:
{
"mcpServers": {
"demo-mcp-server": {
"command": "python",
"args": ["-m", "src.mcp"],
"cwd": "/path/to/my-mcp-server"
}
}
}
Command Line
# STDIO transport (default)
python -m src.mcp --transport stdio
# HTTP transport
python -m src.mcp --transport http --port 8000
# Help
python -m src.mcp --help
Development
Available Make Commands
make help # Show all available commands
# Development
make install # Install dependencies
make install-dev # Install with dev dependencies
make test # Run tests
make test-verbose # Run tests with verbose output
# Running
make run # Run server locally (STDIO)
make run-http # Run server locally (HTTP)
make health # Check server health
# Docker
make docker-build # Build Docker image
make docker-run # Run in Docker (STDIO)
make docker-run-http # Run in Docker (HTTP)
make docker-stop # Stop containers
make docker-clean # Clean containers and images
# Cleanup
make clean # Clean build artifacts
Testing
# Run all tests
make test
# Run with coverage
python -m pytest --cov=src --cov-report=html
# Run specific test
python -m pytest tests/test_cowsay.py -v
Adding New Tools
- Create a new tool file in
src/mcp/tools/ - Implement the tool using FastMCP patterns
- Register it in
src/mcp/server.py - Add tests in
tests/
Example tool structure:
from fastmcp.tools import Tool
def create_my_tool() -> Tool:
async def my_tool(param: str) -> dict:
# Tool implementation
return {"success": True, "result": param}
return Tool(
name="my_tool",
description="Description of my tool",
func=my_tool,
)
Docker
Building
docker-compose build
Running
STDIO mode (for MCP clients):
docker-compose up demo-mcp-server
HTTP mode (for testing):
docker-compose --profile http up demo-mcp-http
Environment Variables in Docker
Create a .env file or set environment variables in docker-compose.yml:
environment:
- APP_NAME=My Custom MCP Server
- LOG_LEVEL=DEBUG
Health Checks
Check server health:
make health
Or directly:
python -c "from src.mcp.server import get_server_health; import json; print(json.dumps(get_server_health(), indent=2))"
License
MIT License - feel free to use this as a starting point for your own MCP servers.
Contributing
This is a demo/seed project. Feel free to fork and extend it for your own needs.
Happy MCP Development! 🐄
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。