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., "@mcp-swedish-weather what's the weather like in Stockholm 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.
MCP server for Swedish weather data using the SMHI Open Data API.
current_weather — Current conditions for any Swedish location
weather_forecast — Hourly forecast up to 72 hours ahead
Related MCP server: mcp-meteo
30+ built-in Swedish cities (Stockholm, Malmö, Gothenburg, Kiruna, Åre...)
Geocoding fallback for any Swedish location
Coordinate input support (lat,lon)
SMHI Wsymb2 weather symbol codes mapped to readable descriptions
{
"mcpServers": {
"swedish-weather": {
"command": "node",
"args": ["/path/to/mcp-swedish-weather/src/index.js"]
}
}
}SMHI Open Data — free, no authentication required. Official Swedish government meteorological data.
This is one of several Nordic MCP servers. See mcp-nordic for the unified meta-server.
MIT
Get current weather for a location in Sweden using SMHI.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | Swedish city name or lat,lon coordinates |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the data source (SMHI) and geographic scope (Sweden), but doesn't mention rate limits, error conditions, authentication needs, or what the response format looks like. For a read operation with zero annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that states the core functionality without any wasted words. It's appropriately sized for a simple tool and front-loads the essential information.
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 single-parameter read tool with good schema coverage but no annotations or output schema, the description provides adequate basic context (what it does, geographic scope, data source). However, it lacks important behavioral details like response format, error handling, or usage guidance relative to the sibling tool, leaving room for improvement.
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 100% description coverage, with the 'location' parameter fully documented as 'Swedish city name or lat,lon coordinates'. The description doesn't add any parameter details beyond what the schema already provides, so it meets the baseline for high schema coverage.
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 action ('Get current weather') and resource ('for a location in Sweden using SMHI'), making the purpose immediately understandable. It doesn't explicitly differentiate from the sibling 'weather_forecast' tool, which prevents a perfect score, but the 'current' vs 'forecast' distinction is implied.
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 the sibling 'weather_forecast' tool, nor does it mention any prerequisites or constraints beyond the location requirement. The agent must infer usage context from the tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get hourly weather forecast for a location in Sweden using SMHI.
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | Swedish city name or lat,lon coordinates | |
| hours | No | Hours ahead (default 24, max 72) |
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 specifies the data source (SMHI) and geographic limitation (Sweden), which adds useful context. However, it doesn't mention rate limits, authentication requirements, error conditions, or what the response format looks like.
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, efficient sentence that communicates the essential information with zero wasted words. It's appropriately sized for a simple tool with good schema documentation and gets straight to the point.
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 relatively simple weather forecast tool with 2 parameters and 100% schema coverage, the description provides adequate context about scope and data source. However, with no output schema and no annotations, it could benefit from mentioning what the forecast data includes (temperature, precipitation, etc.) or the response format.
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%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
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 specific action ('Get'), resource ('hourly weather forecast'), and scope ('for a location in Sweden using SMHI'). It distinguishes from the sibling tool 'current_weather' by specifying it provides forecast data rather than current conditions.
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 about when to use this tool (for hourly forecasts in Sweden via SMHI), but doesn't explicitly state when NOT to use it or mention alternatives beyond the implied sibling tool. The geographic and data source constraints are helpful guidance.
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.
v0.1.0current_weatherweather_forecastScored across 2 tools
The two tools have clearly distinct purposes: one provides current weather data, while the other provides forecast data. There is no overlap or ambiguity between them, as each targets a specific temporal aspect of weather information.
Both tools follow a consistent naming pattern with a descriptive adjective_noun structure (current_weather, weather_forecast). The naming is uniform and predictable, making it easy to understand their functions at a glance.
With only two tools, the server feels thin for a weather domain that typically requires more comprehensive coverage, such as historical data, alerts, or location search. While the tools cover current and forecast data, the scope is limited and may not support complex agent workflows effectively.
The toolset is significantly incomplete for a weather server, lacking essential operations like historical weather data, location search/autocomplete, weather alerts, or multi-day forecasts. This creates gaps that could lead to agent failures when handling broader weather-related queries.
Weather forecasts from MET Norway (Yr): geocoding plus hourly forecasts worldwide.
Global weather via Open-Meteo: forecast, ERA5 archive, marine, air quality, geocoding, elevation.
Real-time weather conditions and multi-day forecasts via Open-Meteo — free, no API key required
OpenWeather MCP — wraps the OpenWeatherMap API (openweathermap.org)