This server provides real-time weather data for locations in the Netherlands using KNMI weather stations. You can:
Get raw weather data (temperature, humidity, wind speed, precipitation, visibility, air pressure)
Receive natural language interpretation of current weather conditions
Search for locations within the Netherlands
Find the nearest KNMI weather station to specific coordinates
The MCP server uses .ENV for managing environment variables, particularly for storing the KNMI API key.
The MCP server utilizes NumPy for numerical operations in processing weather data.
The MCP server uses pandas for data manipulation and analysis of weather information.
The MCP server leverages Pydantic for data validation and settings management.
The MCP server is built with Python and requires Python 3.10 or higher.
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., "@KNMI Weather MCP what's the weather like in Amsterdam right now?"
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.
A FastMCP server that provides real-time weather data from KNMI (Royal Netherlands Meteorological Institute) weather stations. This application fetches the latest 10-minute measurements from the nearest weather station to any location in the Netherlands.
Get weather data for any location in the Netherlands
Automatically finds the nearest KNMI weather station
Provides real-time measurements including:
Temperature
Humidity
Wind speed and direction
Precipitation
Visibility
Air pressure
Natural language interpretation of weather conditions
Location search functionality
Detailed logging
Related MCP server: mcp-server-weather-cuhksz
Python 3.10 or higher
KNMI API Key (get one from KNMI Data Platform)
uv package manager
Clone the repository:
git clone <repository-url>
cd knmi-mcpCreate a .env file in the project root:
KNMI_API_KEY=your_api_key_hereTo use this application with Claude AI, run the following command in the folder of the project:
uv run fastmcp install src/knmi_weather_mcp/server.pyThis will add the following configuration to your Claude configuration file (typically located at ~/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"
]
}
}Note: If you see an error like this:
spawn uv ENOENTReplace the uv command with the full path to the uv command. On *nix systems this can be found with the command which uv.
For development or standalone usage:
uv run fastmcp run src/knmi_weather_mcp/server.pyGet a natural language interpretation of current weather conditions for any location in the Netherlands.
Example:
await what_is_the_weather_like_in("Amsterdam")Get raw weather data for a location.
Example:
await get_location_weather("Rotterdam")Search for locations in the Netherlands.
Example:
await search_location("Utrecht")Find the nearest KNMI weather station to given coordinates.
Example:
await get_nearest_station(52.3676, 4.9041)The application logs are stored in the logs/knmi_weather.log file, providing detailed information about:
API requests and responses
Weather data processing
Error messages
Debug information
This application uses the KNMI Data Platform API to fetch data from the "Actuele10mindataKNMIstations" dataset, which provides 10-minute interval measurements from all KNMI weather stations in the Netherlands.
The application includes robust error handling for:
Invalid locations
API authentication issues
Network problems
Data parsing errors
Missing measurements
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.
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.
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If you are the server author, to access and configure the admin panel.
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