random-number-server
An MCP server that generates random numbers by using national weather data as entropy seeds. It provides a unique way to generate random values through weather API integration within the Model Context Protocol.
README
random-number-server
MCP server to generate random numbers using the national weather data as seeds.
Build Instructions
Local Development Build
# Install uv if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh
# Clone the repository
git clone https://github.com/nobelk/random-number-server.git
cd random-number-server
# Install dependencies and build the project
uv sync
# Install in editable mode for development
uv pip install -e .
Docker Build
# Build the Docker image
docker build -t random-number-server:latest .
# Or use Docker Compose to build
docker-compose build
Quick Start
Using Docker Compose (Recommended)
# Build and run the server
docker-compose up -d
# View logs
docker-compose logs -f
# Stop the server
docker-compose down
Using uv directly
# Install dependencies
uv sync
# Run the server
uv run src/random_server.py
Unit Tests
The project includes comprehensive unit tests for both core modules with 86% code coverage.
Running Tests
# Install dependencies
uv sync
# Run all tests
uv run pytest
# Run tests with verbose output
uv run pytest -v
# Run tests with coverage report
uv run pytest --cov=src --cov-report=term-missing
# Run specific test files
uv run pytest tests/test_random_number_generator.py
uv run pytest tests/test_random_server.py
Test Coverage
- src/RandomNumberGenerator.py: 83% coverage (13 tests)
- src/random_server.py: 92% coverage (17 tests)
- Total: 86% coverage (30 tests)
Tests cover:
- Initialization and configuration
- Random number generation algorithms
- Weather API integration
- Error handling and edge cases
- FastMCP tool registration and execution
- Concurrent request handling
Docker Setup
The project includes Docker and Docker Compose configurations for easy deployment.
Docker Image
- Base: Python 3.13 Alpine (optimized for size)
- Size: ~110MB
- Security: Runs as non-root user
- Build: Multi-stage build for optimization
Docker Compose
# Production
docker-compose up -d
# Development (with live reload)
docker-compose -f docker-compose.yml -f docker-compose.dev.yml up
# Run tests in container
docker-compose run --rm --entrypoint /app/.venv/bin/python random-server -m pytest
See README_DOCKER.md for detailed Docker instructions.
MCP Configuration
Run the MCP server locally
uv --directory /ABSOLUTE/PATH/TO/PARENT/FOLDER/random-number-server run src/random_server.py
Configure Claude Desktop
Edit ~/Library/Application\ Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"weather": {
"command": "/Users/Nobel.Khandaker/.pyenv/shims/uv",
"args": [
"--directory",
"/Users/Nobel.Khandaker/sources/random-number-server",
"run",
"src/random_server.py"
]
}
}
}
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。