This server provides access to weather forecast information for locations in South Korea via the Korea Meteorological Administration API.
Capabilities:
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)
Provide comprehensive weather data including temperature, precipitation, sky condition, humidity, wind direction, and wind speed
Return structured text responses optimized for LLM consumption
Supports containerized deployment of the Korea Weather MCP server, allowing for isolated and consistent execution across different environments.
Enables secure configuration of API credentials for the Korea Meteorological Administration through environment variables.
Stores and provides access to grid coordinates data for Korean administrative regions, enabling location-based weather queries.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Python MCP Korea Weather Service 서울특별시 서초구 양재1동의 날씨는?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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
Related MCP server: MCP Weather Server
Python 3.12+
Korea Meteorological Administration API credentials
You can obtain the API credentials by signing up at the Public Data Portal and requesting access to the "기상청_단기예보 ((구)_동네예보) 조회서비스" API.
Clone the repository:
git clone https://github.com/jikime/py-mcp-ko-weather.git
cd py-mcp-ko-weatheruv installation
curl -LsSf https://astral.sh/uv/install.sh | shCreate a virtual environment and install dependencies:
uv venv -p 3.12
source .venv/bin/activate
uv pip install -r requirements.txtCreate a .env file with your KMA API credentials:
cp env.example .env
vi .env
KO_WEATHER_API_KEY=your_api_key_hereMigrate the grid coordinates data from Excel to SQLite:
uv run src/migrate.pyBuild the Docker image:
docker build -t py-mcp-ko-weather .Run the container:
docker run py-mcp-ko-weatherRun the server:
mcp run src/server.pyRun the MCP Inspector
mcp dev server.pyAdd the server configuration to your MCP settings file:
To install automatically via Smithery:
npx -y @smithery/cli install @jikime/py-mcp-ko-weather --client claudeTo install manually
open ~/Library/Application Support/Claude/claude_desktop_config.json
Add this to the mcpServers object:
{
"mcpServers": {
"Google Toolbox": {
"command": "/path/to/bin/uv",
"args": [
"--directory",
"/path/to/py-mcp-ko-weather",
"run",
"src/server.py"
]
}
}
}open ~/.cursor/mcp.json
Add this to the mcpServers object:
{
"mcpServers": {
"Google Toolbox": {
"command": "/path/to/bin/uv",
"args": [
"--directory",
"/path/to/py-mcp-ko-weather",
"run",
"src/server.py"
]
}
}
}{
"mcpServers": {
"Google Toolbox": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"py-mcp-ko-weather"
]
}
}
}Once configured, you can ask Claude questions like:
"서울특별시 서초구 양재1동의 날씨는?"
"부산광역시 해운대구 우동의 날씨 예보를 알려줘"
"경기도 성남시 분당구의 현재 기온은?"
get_grid_location(city: str, gu: str, dong: str) -> dictRetrieves 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(city: str, gu: str, dong: str, nx: int, ny: int) -> strCalls 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.
GET weather://instructionsProvides detailed documentation on how to use the Korea Weather MCP server, including tool workflows and response formats.
The server includes a structured prompt template for guiding conversations about weather queries, ensuring efficient information gathering and clear presentation of forecast data.
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
...This project is licensed under the MIT License - see the LICENSE file for details.
한국 기상청의 초단기예보 API를 호출하여 특정 지역의 날씨 예보 정보를 제공합니다. 사용자가 입력한 지역 정보와 격자 좌표를 바탕으로 현재 시점에서의 기상 정보를 조회합니다. 이 도구는 온도, 강수량, 하늘상태, 습도, 풍향, 풍속 등 상세한 기상 정보를 포함하며, 6시간 이내의 단기 예보를 제공합니다.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | ||
| gu | Yes | ||
| dong | Yes | ||
| nx | Yes | ||
| ny | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (calls an API, provides weather data) and the timeframe (within 6 hours), but lacks critical behavioral information such as rate limits, authentication requirements, error handling, response format details, or whether this is a read-only operation. For a tool that calls an external API with 5 required parameters, this represents significant gaps in behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise with three sentences that each add value. The first sentence establishes the core functionality, the second explains the input basis, and the third details the output content and timeframe. There's no redundant information, and the structure flows logically from purpose to implementation to output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (5 required parameters, external API call) and the presence of an output schema, the description provides basic contextual information about what the tool does and what data it returns. However, with no annotations and poor parameter documentation, it lacks sufficient information about behavioral aspects, parameter usage, and differentiation from sibling tools. The output schema existence reduces the need to describe return values, but other gaps remain significant.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for all 5 parameters, the description provides minimal parameter semantics. It mentions that the tool uses 'region information and grid coordinates' as input, which vaguely corresponds to the city, gu, dong, nx, and ny parameters, but doesn't explain what each parameter represents, their relationships, valid values, or how they should be formatted. The description fails to compensate for the complete lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it calls the Korea Meteorological Administration's ultra-short-term forecast API to provide weather forecast information for a specific region. It specifies the data source, timeframe (within 6 hours), and types of weather information included (temperature, precipitation, sky conditions, humidity, wind direction, wind speed). However, it doesn't explicitly differentiate from the sibling tool 'get_grid_location' beyond mentioning grid coordinates as input.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 versus alternatives. While it mentions using grid coordinates and region information as input, it doesn't explain when this tool is appropriate compared to the sibling 'get_grid_location' or other potential weather tools. There's no mention of prerequisites, limitations, or specific use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
한국 기상청 API에 사용되는 격자 좌표(nx, ny)를 조회합니다. 사용자가 입력한 시/도, 구/군, 동/읍/면 정보를 바탕으로 해당 지역의 기상청 격자 좌표를 데이터베이스에서 검색하여 반환합니다. 이 도구는 기상청 API 호출에 필요한 정확한 좌표값을 얻기 위해 필수적으로 사용됩니다.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | ||
| gu | Yes | ||
| dong | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the action (retrieves from database), input basis (administrative divisions), and purpose (obtain coordinates for API calls). However, it lacks details on error handling, database limitations, or response format, which are important for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences: first states the tool's purpose, second explains the input-output mapping, third provides usage context. Each sentence adds essential information without redundancy, making it appropriately concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 required parameters, no annotations, but has an output schema), the description is mostly complete. It covers purpose, parameters, and usage context. The output schema likely handles return value documentation, so the description doesn't need to explain outputs. However, it could benefit from more behavioral details like error cases or data freshness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It explicitly explains the meaning of all three parameters: '시/도, 구/군, 동/읍/면 정보' (city/province, district, neighborhood/town/village), clarifying that these are administrative divisions used to search the database. This adds significant value beyond the schema's bare property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '조회합니다' (retrieves) grid coordinates (nx, ny) from a database based on administrative divisions. It specifies the resource (격자 좌표), the source (한국 기상청 API), and distinguishes it from the sibling tool get_forecast by focusing on coordinate lookup rather than weather forecasting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: '기상청 API 호출에 필요한 정확한 좌표값을 얻기 위해 필수적으로 사용됩니다' (essential for obtaining accurate coordinates needed for Korea Meteorological Administration API calls). However, it does not explicitly mention when not to use it or name alternatives beyond the implied distinction from get_forecast.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
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.
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.
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.
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If you are the server author, to access and configure the admin panel.
An MCP server for weather information by @kulybaba
An MCP server for weather information by @kulybaba
MCP server for weather with reasoning — umbrella advice, outdoor checks, city comparisons.
Open-Meteo tabanlı anahtarsız hava durumu tahmin MCP sunucusu.
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