developer-tools

JWeather

juhemcp

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.

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

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 26 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.826 reviews
  • Benjamin Gupta· Dec 20, 2024

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

  • Pratham Ware· Dec 8, 2024

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

  • Chaitanya Patil· Dec 4, 2024

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

  • Piyush G· Nov 23, 2024

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

  • Mia Choi· Nov 11, 2024

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

  • Shikha Mishra· Oct 14, 2024

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

  • Noor Taylor· Oct 2, 2024

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

  • Kwame Iyer· Sep 17, 2024

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

  • Kaira Rao· Aug 8, 2024

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

  • Omar Jain· Jul 27, 2024

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

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