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weather-service MCP Server: Precision Forecasts & Climate Reliability - MCP Implementation

weather-service MCP Server: Precision Forecasts & Climate Reliability

The Weather-Service MCP Server mirrors real-world atmospheric patterns, delivering precise, real-time forecasts to empower data-driven decisions in any climate – reliability you can trust.

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About weather-service MCP Server

What is weather-service MCP Server: Precision Forecasts & Climate Reliability?

This server is a specialized MCP (Model Context Protocol) implementation designed to manage weather-related data and generate actionable summaries. It acts as a centralized hub for storing textual notes, processing climate information, and delivering structured outputs tailored to user-defined detail levels. The system is built to handle both raw data storage and analytical tasks critical for forecasting accuracy and reliability.

How to use weather-service MCP Server: Precision Forecasts & Climate Reliability?

Initialization requires configuring the server via MCP client settings. For development setups, update the claude_desktop_config.json with custom command parameters pointing to your local project directory. Published instances can be activated through pre-configured commands. Users interact with the server by adding notes via the add-note tool and generating summaries using the summarize-notes prompt, specifying style preferences for output granularity.

weather-service MCP Server Features

Key Features of weather-service MCP Server: Precision Forecasts & Climate Reliability?

Data Organization: Implements a note storage system with custom URI schemes (note://) and metadata tracking.
Dynamic Summarization: Generates tailored weather summaries through adjustable style parameters (brief/detailed) that aggregate all stored data points.
Real-Time Updates: Automatic client notifications when new notes are added using the add-note tool, ensuring synchronized state across connected devices.
Deployment Flexibility: Supports both local development environments and production-ready PyPI distributions with dependency management.

Use cases of weather-service MCP Server: Precision Forecasts & Climate Reliability?

• Climate researchers analyzing historical patterns via aggregated note summaries
• Meteorological teams maintaining synchronized forecasting data repositories
• Developers integrating weather data pipelines into MCP-based applications

weather-service MCP Server FAQ

FAQ from weather-service MCP Server: Precision Forecasts & Climate Reliability?

How do I configure development servers? Modify mcpServers block in your client config with the server directory path and execution command.
What debugging tools are recommended? Use the MCP Inspector for stdio-based debugging sessions.
Can I customize summary styles beyond brief/detailed? Current implementation restricts style options to the defined parameters, but extensions can be developed through API customization.
How is data versioning handled? The system focuses on current state management - historical revisions require external logging solutions.
What authentication methods work for PyPI publishing? Support token-based (--token) or username/password credentials via environment variables.

Content

weather-service MCP server

A MCP weather server project

Components

Resources

The server implements a simple note storage system with:

  • Custom note:// URI scheme for accessing individual notes
  • Each note resource has a name, description and text/plain mimetype

Prompts

The server provides a single prompt:

  • summarize-notes: Creates summaries of all stored notes
    • Optional "style" argument to control detail level (brief/detailed)
    • Generates prompt combining all current notes with style preference

Tools

The server implements one tool:

  • add-note: Adds a new note to the server
    • Takes "name" and "content" as required string arguments
    • Updates server state and notifies clients of resource changes

Configuration

[TODO: Add configuration details specific to your implementation]

Quickstart

Install

Claude Desktop

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

Development/Unpublished Servers Configuration
"mcpServers": {
  "weather-service": {
    "command": "uv",
    "args": [
      "--directory",
      "/Users/<you-user-name>/Codes/GitHub/weather_service",
      "run",
      "weather-service"
    ]
  }
}
Published Servers Configuration
"mcpServers": {
  "weather-service": {
    "command": "uvx",
    "args": [
      "weather-service"
    ]
  }
}

Development

Building and Publishing

To prepare the package for distribution:

  1. Sync dependencies and update lockfile:
uv sync
  1. Build package distributions:
uv build

This will create source and wheel distributions in the dist/ directory.

  1. Publish to PyPI:
uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

  • Token: --token or UV_PUBLISH_TOKEN
  • Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /Users/<your-user-name>/Codes/GitHub/weather_service run weather-service

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

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