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Weather MCP Server

by Sukruthr
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Python
Remote

Weather MCP Server

A Model Context Protocol (MCP) server that provides real-time weather information for any location worldwide using the wttr.in service.

Overview

This MCP server exposes a fetch_weather tool that allows AI assistants like Claude to retrieve current weather information for any city or location. It's built using FastMCP and provides a simple, reliable interface for weather data.

Related MCP server: Weather MCP Server

Features

  • 🌤️ Real-time weather information for any location

  • 🌍 Worldwide coverage

  • ⚡ Fast and lightweight

  • 🔌 Easy integration with MCP-compatible clients (Claude Desktop, etc.)

  • 📦 Simple setup with minimal dependencies

Prerequisites

  • Python 3.12 or higher

  • uv package manager (recommended) or pip

Installation

  1. Clone this repository:

git clone <your-repo-url>
cd weather-mcp

  1. Install dependencies using uv:

uv pip install -e .

Or using pip:

pip install -e .

Configuration

Claude Desktop

To use this server with Claude Desktop, add the following configuration to your Claude Desktop config file:

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "weather-bot": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/weather-mcp",
        "run",
        "main.py"
      ]
    }
  }
}

Replace /absolute/path/to/weather-mcp with the actual path to this project directory.

Usage

Once configured, the weather tool will be available to your AI assistant. You can ask questions like:

  • "What's the weather in London?"

  • "Tell me the current weather in New York"

  • "How's the weather in Tokyo?"

Running Standalone

You can also run the server directly for testing:

python main.py

Or test the weather function directly:

python tools/weather.py

Available Tools

fetch_weather

Retrieves current weather information for a specified location.

Parameters:

  • location (string): The city or location name (e.g., "London", "New York", "Tokyo")

Returns:

  • A string containing the weather information including temperature and conditions

Example Response:

London: ⛅️ +12°C

Project Structure

weather-mcp/
├── main.py              # MCP server entry point
├── tools/
│   ├── __init__.py
│   └── weather.py       # Weather fetching logic
├── pyproject.toml       # Project configuration
├── README.md            # This file
└── uv.lock             # Dependency lock file

Technical Details

  • MCP Framework: FastMCP

  • Weather API: wttr.in - a console-oriented weather service

  • Transport: stdio (standard input/output)

  • Python Version: 3.12+

How It Works

  1. The server exposes a fetch_weather tool via the Model Context Protocol

  2. When called, it makes an HTTP request to wttr.in's API

  3. The weather data is formatted and returned to the MCP client

  4. The AI assistant can then present this information naturally to the user

Troubleshooting

Server not appearing in Claude Desktop

  1. Verify the path in your config file is absolute and correct

  2. Restart Claude Desktop completely

  3. Check the Claude Desktop logs for any error messages

Weather data not loading

  • Ensure you have an active internet connection

  • The wttr.in service must be accessible from your network

  • Some corporate networks may block access to external weather services

Acknowledgments

Made with ❤️ for the MCP community

Available Tools

1 tool
fetch_weatherC

Get weather information for a given location

ParametersJSON Schema
NameRequiredDescriptionDefault
locationYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.7/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the basic function without mentioning any behavioral traits like rate limits, data sources, error handling, or response format. This leaves significant gaps in understanding how the tool behaves beyond its core purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with a single sentence that directly states the tool's purpose. It is front-loaded and wastes no words, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter) and the presence of an output schema, the description is minimally adequate. However, it lacks details on behavioral aspects and parameter semantics, which are important even for simple tools. The output schema helps, but the description doesn't fully cover the tool's context without annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, meaning the input schema provides no descriptions for parameters. The description mentions 'for a given location,' which hints at the 'location' parameter but adds minimal semantic value—it doesn't clarify what format the location should be in (e.g., city name, coordinates) or any constraints. This is insufficient to compensate for the lack of schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('weather information') with the scope ('for a given location'), making the purpose specific and understandable. It doesn't need to distinguish from siblings since there are none, but it could be slightly more detailed about what weather information is provided.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool, such as prerequisites, alternatives, or specific contexts. It merely states what it does without indicating any constraints or best practices for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.9/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct as it is the only one available.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern (fetch_weather), and with only one tool, consistency is inherently perfect as there are no other names to compare against.

Tool Count2/5

A single tool is too few for a weather server's apparent scope, which typically involves multiple operations like forecasts, alerts, or historical data. This minimal set feels thin and limits functionality.

Completeness2/5

The tool surface is severely incomplete for a weather domain. It only provides current weather information, with obvious gaps such as missing forecasts, alerts, or location-based queries, which are essential for typical weather-related workflows.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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Related MCP Connectors

Related MCP Servers

  • Weather MCP Server

    wudongjie
    F
    license
    Not graded
    quality
    D
    maintenance
    Provides real-time weather information and forecasts, connecting AI assistants with live weather data for current conditions and multi-day forecasts for any location worldwide.

  • Weather MCP Server

    9renbufu
    A
    license
    Not graded
    quality
    Not graded
    maintenance
    Provides real-time weather information for global locations and 12 major Chinese cities using the wttr.in API. It enables users to query temperature, humidity, and atmospheric conditions through the HelloAgents framework.

  • Weather MCP Server

    zzhRooT1998
    A
    license
    Not graded
    quality
    D
    maintenance
    Enables real-time weather queries for 12 Chinese cities and global cities using English names via the wttr.in API.
    MIT

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