Skip to main content
Glama
jikime

Python MCP Korea Weather Service

by jikime
README.md
# Korea Weather MCP Server

[![smithery badge](https://smithery.ai/badge/@jikime/py-mcp-ko-weather)](https://smithery.ai/server/@jikime/py-mcp-ko-weather) ![](https://badge.mcpx.dev?type=server 'MCP Server') ![Version](https://img.shields.io/badge/version-1.1.10-green) ![License](https://img.shields.io/badge/license-MIT-blue)

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 Excel to SQLite:
```bash
uv run src/migrate.py
```

#### Using Docker

1. Build the Docker image:
```bash
docker build -t py-mcp-ko-weather .
```

2. Run the container:
```bash
docker run py-mcp-ko-weather
```

#### Using Local

1. Run the server:
```bash
mcp run src/server.py
```
2. Run the MCP Inspector
```bash
mcp dev server.py
```

## Configure MCP Settings
Add the server configuration to your MCP settings file:

#### Claude desktop app 
1. To install automatically via [Smithery](https://smithery.ai/server/@jikime/py-mcp-ko-weather):

```bash
npx -y @smithery/cli install @jikime/py-mcp-ko-weather --client claude
```

2. To install manually
open `~/Library/Application Support/Claude/claude_desktop_config.json`

Add this to the `mcpServers` object:
```json
{
  "mcpServers": {
    "Google Toolbox": {
      "command": "/path/to/bin/uv",
      "args": [
        "--directory",
        "/path/to/py-mcp-ko-weather",
        "run",
        "src/server.py"
      ]
    }
  }
}
```

#### Cursor IDE 
open `~/.cursor/mcp.json`

Add this to the `mcpServers` object:
```json
{
  "mcpServers": {
    "Google Toolbox": {
      "command": "/path/to/bin/uv",
      "args": [
        "--directory",
        "/path/to/py-mcp-ko-weather",
        "run",
        "src/server.py"
      ]
    }
  }
}
```

#### for Docker
```json
{
  "mcpServers": {
    "Google Toolbox": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "py-mcp-ko-weather"
      ]
    }
  }
}
```

### Using with Claude

Once configured, you can ask Claude questions like:
- "서울특별시 서초구 양재1동의 날씨는?"
- "부산광역시 해운대구 우동의 날씨 예보를 알려줘"
- "경기도 성남시 분당구의 현재 기온은?"

## API Reference

### Tools

#### Get Grid Location
```
get_grid_location(city: str, gu: str, dong: str) -> dict
```
Retrieves the grid coordinates (nx, ny) used by the Korea Meteorological Administration API for the specified location. 
This tool searches the database for the exact coordinates based on city/province, district/county, and neighborhood/town information.

#### Get Forecast
```
get_forecast(city: str, gu: str, dong: str, nx: int, ny: int) -> str
```
Calls the KMA's ultra-short-term forecast API to provide weather forecast information for a specific location.
Returns comprehensive weather data including temperature, precipitation, sky condition, humidity, wind direction, and wind speed.

### Resources

#### Weather Instructions
```
GET weather://instructions
```
Provides detailed documentation on how to use the Korea Weather MCP server, including tool workflows and response formats.

### Prompts

#### Weather Query
The server includes a structured prompt template for guiding conversations about weather queries, ensuring efficient information gathering and clear presentation of forecast data.

## Response Format

Weather forecast responses are provided in structured text format, optimized for LLM processing:

```
Weather forecast for 서울특별시 서초구 양재1동 (coordinates: nx=61, ny=125)
Date: 2025-05-01
Time: 15:00

Current conditions:
Temperature: 22.3°C
Sky condition: Mostly clear
Precipitation type: None
Precipitation probability: 0%
Humidity: 45%
Wind direction: Northwest
Wind speed: 2.3 m/s

Hourly forecast:
16:00 - Temperature: 21.8°C, Sky: Clear, Precipitation: None
17:00 - Temperature: 20.5°C, Sky: Clear, Precipitation: None
18:00 - Temperature: 19.2°C, Sky: Clear, Precipitation: None
...
```

## Acknowledgements

- [Korea Meteorological Administration](https://www.kma.go.kr/)
- [Public Data Portal](https://www.data.go.kr/)
- [MCP Protocol](https://github.com/mcp-foundation/mcp-spec)

## License

This project is licensed under the MIT License - see the LICENSE file for details.

TDQS

B3.4/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get_forecast retrieves weather forecast data, while get_grid_location provides grid coordinates needed for API calls. There is no overlap in functionality, and an agent can easily tell them apart based on their descriptions.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern (get_forecast, get_grid_location). The naming is predictable and readable, with no deviations or mixed conventions.

Tool Count2/5

With only two tools, the server feels thin for a weather service domain. While the tools cover forecast retrieval and coordinate lookup, there are likely gaps in functionality (e.g., historical data, alerts, or broader regional coverage) that could limit agent workflows.

Completeness2/5

The tool set is severely incomplete for a weather service. It lacks essential operations such as historical weather data, severe weather alerts, multi-day forecasts, or location search beyond grid coordinates. Agents will face dead ends when trying to perform common weather-related tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues