KNMI MCP Server
Click on "Deploy 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 MCP Serverwhat's the weather forecast for Amsterdam this weekend?"
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.
KNMI MCP Server
A Model Context Protocol (MCP) server that provides access to Dutch weather data via the KNMI (Royal Netherlands Meteorological Institute) API.
Overview
This MCP server enables AI assistants and applications to access comprehensive Dutch weather information, including current conditions, forecasts, alerts, and historical data. It automatically handles location name resolution, converting human-readable Dutch city names into KNMI grid identifiers.
Related MCP server: mcp-danish-weather
MCP Tools
get_weather
Get comprehensive weather information for any Dutch location.
Parameters:
location(string, required): Dutch location name (e.g., "Amsterdam", "De Bilt"). Will (fuzzy) matched to a well-known/mapped locality.region(number, optional): Region number (0-15) for additional regional context
Returns:
Current weather conditions and temperature
Hourly forecast (next 48 hours)
Daily forecast (next 7 days)
Weather alerts and warnings
Wind conditions (speed, direction, gusts)
Precipitation data (amount, chance)
UV index information
Sunrise and sunset times
Weather backgrounds for visual representation
MCP Client Configuration
Add to your MCP host configuration (e.g. Claude Desktop, VS Code, etcetera):
{
"knmi-weather": {
"command": "npx",
"args": ["-y", "@dstotijn/knmi-mcp"]
}
}Weather Data Structure
Current Conditions
Temperature (°C)
Weather type code
Wind speed, direction, and gusts
Precipitation amount and probability
UV index with safety recommendations
Forecasts
Hourly: Next 48 hours with detailed conditions
Daily: Next 7 days with min/max temperatures and precipitation
Alerts
Alert levels: none, potential, yellow, orange, red
Detailed descriptions and safety advice
Affected regions and time periods
Additional Data
Sunrise and sunset times
Weather backgrounds for UI visualization
Regional weather information
Historical climate data context
License
©️ 2025 David Stotijn
Available Tools
1 toolget_weatherGet WeatherARead-only
Get current weather information for a Dutch location. The location parameter accepts location names (e.g., 'Amsterdam', 'De Bilt') and automatically converts them to the required grid identifiers.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | Location name (e.g., 'Amsterdam', 'De Bilt') | |
| region | No | Optional region number (0-15) |
Output Schema
| Name | Required | Description |
|---|---|---|
| sun | No | Sunrise and sunset times |
| wind | No | Wind information for the current weather |
| daily | No | Daily forecast for the weather |
| alerts | Yes | Alerts for this location in the next 48 hours |
| hourly | No | Hourly forecast for the weather |
| uvIndex | No | UV index data for the current weather |
| summaries | Yes | Summary of the current weather conditions to be used |
| backgrounds | No | The weather backgrounds to be used |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations provide readOnlyHint=true, indicating this is a safe read operation. The description adds value by explaining that location names are automatically converted to grid identifiers, which is useful behavioral context not covered by annotations. However, it does not disclose other potential traits like rate limits, error handling, or data freshness, leaving some gaps in transparency despite the annotation support.
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 well-structured, consisting of two sentences that efficiently convey the tool's purpose and key behavioral detail (automatic conversion of location names). Every sentence adds value without redundancy, and the information is front-loaded, making it easy to grasp quickly. There is no wasted verbiage or unnecessary elaboration.
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 (2 parameters, 1 required), the presence of annotations (readOnlyHint), and an output schema (which handles return values), the description is reasonably complete. It covers the core functionality and a key behavioral aspect (location conversion). However, it could be more comprehensive by addressing potential limitations or usage nuances, such as the scope of supported locations or the role of the optional region parameter.
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?
Schema description coverage is 100%, meaning the input schema fully documents both parameters (location and region). The description adds semantic context by clarifying that location names are accepted and automatically converted to grid identifiers, which enhances understanding beyond the schema's basic description. This provides some added value, but since the schema already covers the parameters well, the baseline score of 3 is appropriate.
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: 'Get current weather information for a Dutch location.' It specifies the verb ('Get'), resource ('weather information'), and geographic scope ('Dutch location'), making it easy to understand what the tool does. However, since there are no sibling tools, it cannot demonstrate differentiation from alternatives, which prevents a perfect score.
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 implies usage by specifying 'for a Dutch location,' which sets a geographic context. However, it does not provide explicit guidance on when to use this tool versus alternatives (e.g., other weather tools or methods), nor does it mention any exclusions or prerequisites. With no sibling tools, the lack of comparative guidance is less critical, but the description still only offers implied context without clear when/when-not instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- Changed
get_weather1 field changed- removed
Input schema / additionalPropertiesRemoved value: -false
1 tool update
- First observed
get_weather
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'get_weather' has a clear and distinct purpose, making disambiguation trivial.
The single tool name 'get_weather' follows a clear verb_noun pattern (get + weather). Since there is only one tool, consistency is inherently perfect with no deviations to evaluate.
A single tool is too few for a weather server's apparent scope, which typically involves multiple operations like forecasts, historical data, or alerts. This minimal set limits functionality and feels incomplete for the domain.
The tool set is severely incomplete for a weather server, covering only current weather retrieval. Obvious gaps include forecasts, historical data, alerts, or multi-location queries, which are essential for comprehensive weather-related tasks.
Maintenance
Related MCP Connectors
Weather forecasts from MET Norway (Yr): geocoding plus hourly forecasts worldwide.
Global weather via Open-Meteo: forecast, historical, marine, air quality, geocoding, elevation.
OpenWeather MCP — wraps the OpenWeatherMap API (openweathermap.org)
Pirate Weather forecast API (Dark Sky-compatible). Free key required.
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