Korea Weather MCP Server

Overview

The Korea Weather MCP Server bridges the gap between Korea Meteorological Administration (KMA) short‑term forecast data and AI assistants such as Claude or Cursor. By exposing the KMA API through the Model Context Protocol, it enables developers to embed real‑time weather information directly into conversational agents without handling authentication or data parsing themselves. This server solves the common problem of integrating external weather services into AI workflows: it abstracts API key management, request throttling, and data normalization behind a simple MCP interface.

At its core, the server fetches weather predictions from KMA’s short‑term forecast API and transforms them into a standardized MCP payload. The payload includes location identifiers, timestamps, temperature, precipitation probability, wind speed, and other key metrics. Developers can then query the server with natural language prompts—such as “What’s the weather at Jeju International Airport?” or “Should I bring an umbrella to Seoul tomorrow afternoon?”—and the AI assistant can retrieve and incorporate accurate, up‑to‑date weather facts into its responses. This eliminates the need for custom parsers or manual API calls within the assistant’s logic.

Key features of the server include:

• Seamless KMA API integration: Handles authentication with an API key and respects rate limits automatically.
• MCP‑compliant data format: Provides weather information in a structure that is immediately consumable by any MCP‑capable client.
• Easy registration: Supports quick installation via Smithery or manual deployment, with environment variable configuration for the API key.
• Cross‑platform compatibility: Works with both desktop and web-based AI assistants, allowing consistent weather data access across devices.

Typical use cases span a wide range of applications. A travel chatbot can recommend itineraries based on upcoming weather, a smart home assistant can adjust HVAC settings or garden watering schedules, and an e‑commerce platform can suggest clothing options for a user’s local forecast. In educational settings, students can query real‑time weather to practice data interpretation or climate science projects. Because the server returns precise, location‑specific forecasts, it is particularly valuable for services that need to make time‑sensitive decisions—such as event planning, logistics, or public safety alerts.

The server’s integration into AI workflows is straightforward: once registered in the assistant’s MCP configuration, any prompt that references weather can be automatically routed to the Korea Weather server. The assistant receives a structured response, merges it with its internal knowledge base, and delivers a coherent answer. This tight coupling reduces latency and eliminates the need for additional middleware or custom API wrappers, giving developers a plug‑and‑play solution that enhances the intelligence and usefulness of their AI applications.