developer-tools

JWeather

by juhemcp

JWeather offers real-time conditions & forecasts via an asyncio Python server, integrating with top weather services. Au

Integrates with weather data services to provide real-time conditions and forecasts through an asyncio-powered Python server with automated CI/CD workflows.

github stars

0

Asyncio-powered for performanceAutomated CI/CD workflows

best for

  • / Developers building weather-aware applications
  • / Automating weather-based workflows
  • / Adding weather context to AI assistants

capabilities

  • / Fetch current weather conditions
  • / Get weather forecasts
  • / Query weather data by location
  • / Access real-time meteorological information

what it does

Provides real-time weather data and forecasts through a Python-based MCP server that integrates with weather APIs.

about

JWeather is a community-built MCP server published by juhemcp that provides AI assistants with tools and capabilities via the Model Context Protocol. JWeather offers real-time conditions & forecasts via an asyncio Python server, integrating with top weather services. Au It is categorized under developer tools.

how to install

You can install JWeather 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

MIT

JWeather is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Juhe Weather MCP Server

一个提供全国天气预报查询功能的模型上下文协议(Model Context Protocol)服务器。该服务器使大型语言模型(LLMs)能够获取全国城市、地区的天气预报情况。

Components

Tools

服务器实现了一个工具:

  • query_weather: 根据城市、地区、区县名称查询当地实时天气预报情况.
    • 需要传入 "city"(城市、区县等名称)作为必须的字符串参数。
async def query_weather(
    city: str = Field(description="查询的城市名称,如北京、上海、广州、深圳、泰顺等;城市或区县或地区名使用简写,严格按照规范填写,否则会导致查询失败")
) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:

Install

This server requires Python 3.10 or higher. Install dependencies using uv (recommended) or pip

Using uv (recommended)

When using uv no specific installation is needed. We will use uvx to directly run jweather-mcp-server.

uvx jweather-mcp-server

Using PIP

Alternatively you can install jweather-mcp-server via pip:

pip install jweather-mcp-server

After installation, you can run it as a script using:

python -m jweather_mcp_server

Configuration

Environment Variables

JUHE_WEATHER_API_KEY: 聚合数据的天气预报查询API密钥。获取:https://www.juhe.cn/docs/api/id/73

JUHE_WEATHER_API_KEY=your_api_key

Configure For CLINE

<details> <summary>Using uvx</summary>
"mcpServers": {
  "jweather-mcp-server": {
    "command": "uvx",
    "args": [
      "jweather-mcp-server"
    ],
    "env": {
      "JUHE_WEATHER_API_KEY": "your_api_key"
    }
  }
}
</details> <details> <summary>Using pip installation</summary>
"mcpServers": {
  "jweather-mcp-server": {
    "command": "python",
    "args": [
      "-m",
      "jmobile_location_mcp_server"
    ],
    "env": {
      "JUHE_WEATHER_API_KEY": "your_api_key"
    }
  }
}
</details>

Debugging

You can use the MCP inspector to debug the server. For uvx installations:

npx @modelcontextprotocol/inspector uvx jweather-mcp-server 

Or if you've installed the package in a specific directory or are developing on it:

cd path/to/servers/src/jweather-mcp-server
npx @modelcontextprotocol/inspector uv run jweather-mcp-server

Examples of Questions for Cline

  1. "查询下苏州的天气"
  2. "今天上海的天气如何?"

FAQ

What is the JWeather MCP server?
JWeather is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for JWeather?
This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
MCP server reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

    JWeather is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Piyush G· Sep 9, 2024

    We evaluated JWeather against two servers with overlapping tools; this profile had the clearer scope statement.

  • Chaitanya Patil· Aug 8, 2024

    Useful MCP listing: JWeather is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Sakshi Patil· Jul 7, 2024

    JWeather reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Ganesh Mohane· Jun 6, 2024

    I recommend JWeather for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Oshnikdeep· May 5, 2024

    Strong directory entry: JWeather surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Dhruvi Jain· Apr 4, 2024

    JWeather has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Rahul Santra· Mar 3, 2024

    According to our notes, JWeather benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Pratham Ware· Feb 2, 2024

    We wired JWeather into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Yash Thakker· Jan 1, 2024

    JWeather is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.