Korea Weather
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"iflow-mcp-jikime-py-mcp-ko-weather","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# Korea Weather MCP Server [](https://smithery.ai/server/@jikime/py-mcp-ko-weather)    This MCP (Multi-platform Communication Protocol) server provides access to Korea Meteorological Administration (KMA) APIs, allowing AI agents to retrieve weather forecast information for locations in South Korea. ## Overview - Retrieve precise grid coordinates for Korean administrative regions - Get detailed short-term weather forecasts for any location in Korea - Support for all Korean administrative divisions (city, district, neighborhood) - Structured text responses optimized for LLM consumption - Comprehensive weather data including temperature, precipitation, sky condition, humidity, wind direction, and wind speed ## Table of Contents - [Prerequisites](#prerequisites) - [Installation](#installation) - [Configure MCP Settings](#configure-mcp-settings) - [API Reference](#api-reference) - [Acknowledgements](#acknowledgements) - [License](#license) ## Setup ### Prerequisites - Python 3.12+ - Korea Meteorological Administration API credentials - You can obtain the API credentials by signing up at the [Public Data Portal](https://www.data.go.kr/) and requesting access to the "기상청_단기예보 ((구)_동네예보) 조회서비스" API. ### Installation 1. Clone the repository: ```bash git clone https://github.com/jikime/py-mcp-ko-weather.git cd py-mcp-ko-weather ``` 2. uv installation ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` 3. Create a virtual environment and install dependencies: ```bash uv venv -p 3.12 source .venv/bin/activate uv pip install -r requirements.txt ``` 4. Create a `.env` file with your KMA API credentials: ``` cp env.example .env vi .env KO_WEATHER_API_KEY=your_api_key_here ``` 5. Migrate the grid coordinates data from Exc…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.