该 MCP（多平台通信协议）服务器提供对韩国气象局 (KMA) API 的访问，允许 AI 代理检索韩国各地的天气预报信息。
检索韩国行政区域的精确网格坐标
获取韩国任何地点的详细短期天气预报
支持韩国所有行政区划（市、区、街道）
针对 LLM 使用进行了优化的结构化文本响应
综合天气数据，包括温度、降水、天空状况、湿度、风向和风速
Related MCP server: MCP Weather Server
Python 3.12+
韩国气象局 API 凭证
您可以通过在公共数据门户注册并请求访问“기상청_단기예보 ((구)_동네예보) 조회서비스”API 来获取 API 凭据。
克隆存储库：
git clone https://github.com/jikime/py-mcp-ko-weather.git
cd py-mcp-ko-weather紫外线安装
curl -LsSf https://astral.sh/uv/install.sh | sh创建虚拟环境并安装依赖项：
uv venv -p 3.12
source .venv/bin/activate
uv pip install -r requirements.txt使用您的 KMA API 凭据创建一个.env文件：
cp env.example .env
vi .env
KO_WEATHER_API_KEY=your_api_key_here将网格坐标数据从 Excel 迁移到 SQLite：
uv run src/migrate.py构建 Docker 镜像：
docker build -t py-mcp-ko-weather .运行容器：
docker run py-mcp-ko-weather运行服务器：
mcp run src/server.py将服务器配置添加到您的 MCP 设置文件：
要通过Smithery自动安装：
npx -y @smithery/cli install @jikime/py-mcp-ko-weather --client claude要手动安装，请打开~/Library/Application Support/Claude/claude_desktop_config.json
将其添加到mcpServers对象：
{
"mcpServers": {
"Google Toolbox": {
"command": "/path/to/bin/uv",
"args": [
"--directory",
"/path/to/py-mcp-ko-weather",
"run",
"src/server.py"
]
}
}
}打开~/.cursor/mcp.json
将其添加到mcpServers对象：
{
"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"
]
}
}
}配置完成后，您可以向 Claude 询问以下问题：
“서울특별시 서초구 양재1동의 날씨는？”
“부산광역시 해운대구 우동의 날씨 예보를 알려줘”
“경기도 성남시 분당구의 현재 기온은？”
get_grid_location(city: str, gu: str, dong: str) -> dict检索韩国气象局 API 针对指定位置使用的网格坐标 (nx, ny)。此工具会根据市/道、区/县以及街区/城镇信息，在数据库中搜索精确坐标。
get_forecast(city: str, gu: str, dong: str, nx: int, ny: int) -> str调用韩国气象局的超短期预报 API，提供特定地点的天气预报信息。返回包括温度、降水量、天空状况、湿度、风向和风速在内的综合天气数据。
GET http://localhost:8000/weather-instructions提供有关如何使用韩国天气 MCP 服务器的详细文档，包括工具工作流程和响应格式。
该服务器包含一个结构化的提示模板，用于引导有关天气查询的对话，确保高效的信息收集和清晰的预报数据呈现。
天气预报响应以结构化文本格式提供，针对 LLM 处理进行了优化：
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
...该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅 LICENSE 文件。
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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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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