FastMCP 服务器提供来自荷兰皇家气象局 (KNMI) 气象站的实时气象数据。该应用程序可从距离荷兰境内任意地点最近的气象站获取最近 10 分钟的测量数据。
获取荷兰任何地点的天气数据
自动查找最近的 KNMI 气象站
提供实时测量，包括：
温度
湿度
风速和风向
沉淀
能见度
气压
天气状况的自然语言解释
位置搜索功能
详细日志记录
Related MCP server: mcp-server-weather-cuhksz
Python 3.10 或更高版本
KNMI API 密钥（从KNMI 数据平台获取）
uv包管理器
克隆存储库：
git clone <repository-url>
cd knmi-mcp在项目根目录中创建一个.env文件：
KNMI_API_KEY=your_api_key_here要将此应用程序与 Claude AI 一起使用，请在项目文件夹中运行以下命令：
uv run fastmcp install src/knmi_weather_mcp/server.py这会将以下配置添加到您的 Claude 配置文件（通常位于~/Library/Application Support/Claude/claude_desktop_config.json ）：
{
"KNMI Weather": {
"command": "uv",
"args": [
"run",
"--with",
"fastmcp",
"--with",
"httpx",
"--with",
"netCDF4",
"--with",
"numpy",
"--with",
"pandas",
"--with",
"pydantic",
"--with",
"python-dotenv",
"--with",
"xarray",
"fastmcp",
"run",
"/Users/<username>/<git location>/knmi-mcp/src/knmi_weather_mcp/server.py"
]
}
}注意：如果您看到如下错误：
spawn uv ENOENT将uv命令替换为uv命令的完整路径。在 *nix 系统上，可以使用命令which uv找到。
对于开发或独立使用：
uv run fastmcp run src/knmi_weather_mcp/server.py获取荷兰任何地点当前天气状况的自然语言解释。
例子：
await what_is_the_weather_like_in("Amsterdam")获取某个地点的原始天气数据。
例子：
await get_location_weather("Rotterdam")搜索荷兰的地点。
例子：
await search_location("Utrecht")查找距离给定坐标最近的 KNMI 气象站。
例子：
await get_nearest_station(52.3676, 4.9041)应用程序日志存储在logs/knmi_weather.log文件中，提供有关以下内容的详细信息：
API 请求和响应
气象数据处理
错误消息
调试信息
该应用程序使用 KNMI 数据平台 API 从“Actuele10mindataKNMIstations”数据集中获取数据，该数据集提供荷兰所有 KNMI 气象站的 10 分钟间隔测量数据。
该应用程序包括强大的错误处理功能：
无效位置
API 身份验证问题
网络问题
数据解析错误
缺少测量值
Get current weather data for a location
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must convey behavioral traits. It only states it gets weather data, but omits details like side effects (none assumed), rate limits, or data freshness. The lack of any behavioral disclosure beyond the basic operation is a gap.
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 a single sentence, which is concise but severely under-specified. It does not earn its place by providing necessary detail; important information is omitted.
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?
For a simple tool with one parameter and no output schema, the description is still very incomplete. It does not explain what 'current weather data' includes, how the response is structured, or any other details needed for effective use.
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 single required parameter 'location' has no description in the schema (0% coverage), and the tool description does not clarify the expected format (e.g., city name, coordinates). This leaves the agent uncertain about how to provide the location value.
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 'Get current weather data for a location' clearly states the verb and resource. It is specific and straightforward, but does not differentiate from sibling tools like 'what_is_the_weather_like_in' which have similar purpose.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, appropriate contexts, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find the nearest KNMI weather station to given coordinates
Args:
latitude: Latitude in degrees
longitude: Longitude in degrees
| Name | Required | Description | Default |
|---|---|---|---|
| latitude | Yes | ||
| longitude | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states what the tool does with no details on return format, error handling, or constraints (e.g., coordinate ranges, station selection logic).
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 concise and front-loaded with a one-sentence purpose followed by a clear Args section. Every element is necessary with no redundancy.
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?
For a simple tool with 2 parameters and no output schema, the description covers the basic operation. However, it lacks details about return format or how the station is identified, which an agent would need to use the result.
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, the description adds some meaning by stating 'Latitude in degrees' and 'Longitude in degrees', but this is minimal. The schema already has titles; the description just adds units, which is basic.
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 finds the nearest KNMI weather station to given coordinates, using the verb 'Find' and specifying the resource. It distinguishes from siblings like get_location_weather which retrieves weather data, not station information.
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. It does not mention prerequisites, limitations, or scenarios where a sibling tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for locations in the Netherlands
Args:
query: Search term for location
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It mentions 'Search' (implying read-only) but offers no details on behavior like pagination, result limits, or response structure. This leaves the agent guessing about side effects or constraints.
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 very short (two sentences) but minimally structured with an Args section. While concise, the brevity comes at the cost of informativeness, and the docstring format is acceptable but not exemplary.
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 single parameter, lack of output schema, and no annotations, the description should cover usage context. It does not explain what the search returns (e.g., locations, coordinates) or any limitations, making it incomplete for effective tool invocation.
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, the description must compensate. It merely paraphrases the parameter as 'search term for location' without adding format, examples, or constraints beyond the schema, which already lists 'query' as a string.
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 searches for locations in the Netherlands, specifying the verb 'Search' and the resource 'locations'. However, it does not differentiate from sibling tools like get_location_weather or what_is_the_weather_like_in, which could also involve location queries.
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?
There is no guidance on when to use this tool versus its siblings, nor any exclusions or prerequisites. The description simply restates the function without context on alternatives or scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get and interpret weather data for a location in the Netherlands
Args:
location: City or place name in the Netherlands
Returns:
A natural language interpretation of the current weather conditions
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral traits. It states the tool returns a natural language interpretation, which is helpful, but it omits critical details like whether the call is read-only, required permissions, or any side effects. The description is basic and lacks transparency beyond the immediate function.
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 concise, consisting of four short lines that front-load the main purpose. It avoids unnecessary words and is well-structured with an Args section. The brevity is appropriate for a simple tool, though it could be more structured with bullet points.
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 has only one parameter, no output schema, and no annotations, the description covers the essential aspects: what it does, the input (location), and the output (natural language interpretation). It also specifies the geographic scope. While it could mention error handling or rate limits, for a simple weather tool the completeness is sufficient.
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 only provides the parameter name and type (string). The description adds meaningful context: 'location: City or place name in the Netherlands' specifies the format and geographic scope. This compensates for the 0% schema description coverage and helps the agent understand the expected input.
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 gets and interprets weather data for a location in the Netherlands. The verb 'get and interpret' combined with the resource 'weather data' makes the purpose specific. It distinguishes from siblings like 'get_location_weather' by emphasizing interpretation and geographic scope, though the difference is not explicitly clarified.
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 does not provide any guidance on when to use this tool versus alternatives such as 'get_location_weather'. No conditions for use or exclusions are mentioned. The phrase 'for a location in the Netherlands' implies geographic limitation but does not address when to choose this over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0get_location_weatherget_nearest_stationsearch_locationwhat_is_the_weather_like_inScored across 4 tools
Tools are mostly distinct: search_location and get_nearest_station are clearly supporting, but get_location_weather and what_is_the_weather_like_in both provide current weather, albeit in different formats (data vs. natural language). This overlap could cause misselection.
Three tools follow verb_noun pattern (get_location_weather, get_nearest_station, search_location), but what_is_the_weather_like_in deviates entirely into a conversational question, breaking consistency.
With 4 tools, the server is well-scoped for a focused weather service covering location search, station lookup, and current conditions. Each tool serves a clear purpose without being excessive.
The tool set covers location search and current weather but lacks forecast, historical data, or station metadata. The domain of weather for the Netherlands has notable gaps that could hinder some agent tasks.
MCP server for weather with reasoning — umbrella advice, outdoor checks, city comparisons.
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
MCP server for structured Dutch vehicle data and license plate intelligence. Access RDW-based vehicle specifications, registration details, APK information, fuel and emissions data, weights, dimensions, ownership-related signals and other vehicle knowledge through KentekenKompas.nl. Built for AI assistants, agents and applications that need reliable, machine-readable information about vehicles registered in the Netherlands.
An MCP server for weather information by @kulybaba
