
Caiyun Weather
Provides real-time weather data and forecasts from Caiyun Weather API using geographic coordinates or location names.
Integrates with Caiyun Weather API to provide real-time weather data and forecasts based on geographic coordinates or location names for travel planning and outdoor activities.
What it does
- Get current weather conditions (temperature, humidity, wind, pressure)
- Retrieve hourly forecasts for next 24+ hours
- Access daily weather forecasts for multiple days
- Check minute-by-minute precipitation for next 2 hours
- Get weather alerts and warnings
- Query air quality trends and pollution data
Best for
About Caiyun Weather
Caiyun Weather is a community-built MCP server published by marcusbai that provides AI assistants with tools and capabilities via the Model Context Protocol. Get real-time weather data and forecasts with Caiyun Weather API, ideal for travel planning and outdoor activities using It is categorized under analytics data.
How to install
You can install Caiyun Weather in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
Caiyun Weather is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
彩云天气 MCP 服务器
基于彩云天气 API 的 Model Context Protocol (MCP) 服务器,提供天气数据查询功能。
功能特点
- 实时天气数据:温度、湿度、风速、气压、能见度等
- 分钟级降水预报:未来2小时的降水情况
- 小时级天气预报:未来24小时或更长时间的天气预报
- 每日天气预报:未来多天的天气预报
- 天气预警信息:各类天气预警
- 空气质量趋势:24小时空气质量变化趋势和主要污染物分析
- 详细生活指数:运动、旅行、洗车、穿衣等详细生活建议
- 降水类型识别:区分雨、雪、雨夹雪、冰雹等降水类型
- 地址查询:支持通过地址查询天气,内置35个主要城市坐标缓存,无需额外配置即可使用
- 多语言支持:支持中文和英文
- 单位制选择:支持公制和英制
安装
安装 Smithery
通过 Smithery 安装 彩云天气 对于Claude的桌面应用:
npm install @smithery/cli -g
smithery install @pepperai/caiyun-weather-mcp
通过 NPX 使用
您可以直接通过 NPX 运行:
npx caiyun-weather-mcp --api-key=您的彩云天气API密钥
或者设置环境变量:
CAIYUN_API_KEY=您的密钥 npx caiyun-weather-mcp
从源码安装
- 克隆仓库:
git clone https://github.com/marcusbai/caiyun-weather-mcp.git
cd caiyun-weather-mcp
- 安装依赖:
npm install
注意:本项目依赖于 Model Context Protocol (MCP) SDK,该SDK需要在运行环境中可用。MCP SDK通常由Claude或其他支持MCP的应用程序提供。
- 构建项目:
npm run build
配置
在使用前,需要配置彩云天气API密钥。地址查询功能支持内置城市缓存,高德地图API密钥为推荐配置。
彩云天气API密钥
- 访问 彩云天气开发者中心
- 注册并登录账号
- 创建应用并获取API密钥
高德地图API密钥(推荐)
- 访问 高德开放平台
- 注册并登录账号
- 创建应用并获取API密钥,需要启用"地理编码"服务
💡 提示:高德地图API密钥为推荐配置。系统内置了35个主要城市的坐标缓存,包括所有直辖市、省会城市和经济发达城市,无需额外配置即可使用。
地址解析功能
支持的城市
系统内置了以下35个主要城市的坐标缓存:
直辖市:北京、上海、天津、重庆
省会及主要城市:广州、深圳、杭州、南京、武汉、成都、西安、长沙、沈阳、大连、青岛、厦门、苏州、郑州、济南、哈尔滨、石家庄、太原、合肥、南昌、福州、南宁、昆明、贵阳、兰州、西宁、拉萨、呼和浩特、海口、银川、乌鲁木齐
智能地址匹配
支持多种地址格式和智能匹配:
- 标准格式:
上海、上海市 - 详细地址:
上海市浦东新区、北京市朝阳区 - 城市别名:
魔都→上海、帝都→北京、羊城→广州、鹏城→深圳等
地址解析策略
- 缓存优先:内置城市坐标立即返回
- API增强:配置高德API后支持任意地址
- 降级处理:未知地址返回北京坐标并提示配置
配置MCP设置
编辑MCP设置文件,添加彩云天气MCP服务器配置:
{
"mcpServers": {
"caiyun-weather": {
"command": "node",
"args": ["完整路径/caiyun-weather-mcp/dist/index.js"],
"env": {
"CAIYUN_API_KEY": "您的彩云天气API密钥",
"AMAP_API_KEY": "您的高德地图API密钥(推荐)"
},
"disabled": false,
"autoApprove": []
}
}
}
如果您通过 NPX 安装了本服务,可以使用以下配置:
{
"mcpServers": {
"caiyun-weather": {
"command": "npx",
"args": ["caiyun-weather-mcp"],
"env": {
"CAIYUN_API_KEY": "您的彩云天气API密钥",
"AMAP_API_KEY": "您的高德地图API密钥(推荐)"
},
"disabled": false,
"autoApprove": []
}
}
}
使用示例
根据经纬度获取天气信息
<use_mcp_tool>
<server_name>caiyun-weather</server_name>
<tool_name>get_weather_by_location</tool_name>
<arguments>
{
"longitude": 116.3976,
"latitude": 39.9075,
"daily_steps": 5,
"hourly_steps": 24,
"language": "zh_CN",
"unit": "metric"
}
</arguments>
</use_mcp_tool>
根据地址获取天气信息
<use_mcp_tool>
<server_name>caiyun-weather</server_name>
<tool_name>get_weather_by_address</tool_name>
<arguments>
{
"address": "上海市",
"daily_steps": 5,
"hourly_steps": 24,
"language": "zh_CN",
"unit": "metric"
}
</arguments>
</use_mcp_tool>
获取实时天气数据
<use_mcp_tool>
<server_name>caiyun-weather</server_name>
<tool_name>get_realtime_weather</tool_name>
<arguments>
{
"longitude": 116.3976,
"latitude": 39.9075,
"language": "zh_CN",
"unit": "metric"
}
</arguments>
</use_mcp_tool>
获取分钟级降水预报
<use_mcp_tool>
<server_name>caiyun-weather</server_name>
<tool_name>get_minutely_forecast</tool_name>
<arguments>
{
"longitude": 116.3976,
"latitude": 39.9075,
"language": "zh_CN",
"unit": "metric"
}
</arguments>
</use_mcp_tool>
获取小时级天气预报
<use_mcp_tool>
<server_name>caiyun-weather</server_name>
<tool_name>get_hourly_forecast</tool_name>
<arguments>
{
"longitude": 116.3976,
"latitude": 39.9075,
"hourly_steps": 24,
"language": "zh_CN",
"unit": "metric"
}
</arguments>
</use_mcp_tool>
获取每日天气预报
<use_mcp_tool>
<server_name>caiyun-weather</server_name>
<tool_name>get_daily_forecast</tool_name>
<arguments>
{
"longitude": 116.3976,
"latitude": 39.9075,
"daily_steps": 5,
"language": "zh_CN",
"unit": "metric"
}
</arguments>
</use_mcp_tool>
获取天气预警信息
<use_mcp_tool>
<server_name>caiyun-weather</server_name>
<tool_name>get_weather_alert</tool_name>
<arguments>
{
"longitude": 116.3976,
"latitude": 39.9075,
"language": "zh_CN",
"unit": "metric"
}
</arguments>
</use_mcp_tool>
获取空气质量趋势
<use_mcp_tool>
<server_name>caiyun-weather</server_name>
<tool_name>get_air_quality_trend</tool_name>
<arguments>
{
"longitude": 116.3976,
"latitude": 39.9075,
"language": "zh_CN",
"unit": "metric"
}
</arguments>
</use_mcp_tool>
获取详细生活指数
<use_mcp_tool>
<server_name>caiyun-weather</server_name>
<tool_name>get_detailed_life_index</tool_name>
<arguments>
{
"longitude": 116.3976,
"latitude": 39.9075,
"language": "zh_CN",
"unit": "metric"
}
</arguments>
</use_mcp_tool>
参数说明
通用参数
longitude:经度latitude:纬度address:地址(仅用于get_weather_by_address)daily_steps:每日预报天数(1-15,默认5)hourly_steps:小时预报数量(1-360,默认24)language:语言(zh_CN或en_US,默认zh_CN)unit:单位制(metric或imperial,默认metric)
许可证
MIT
Alternatives
Related Skills
Browse all skillsTransform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent analysis
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
Comprehensive backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).