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., "@mcp-norwegian-weather What's the weather like in Oslo 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 Norwegian weather data using MET Norway Locationforecast 2.0 (the API behind yr.no).
current_weather — Current conditions for any Norwegian location
weather_forecast — Hourly forecast up to 72 hours ahead
Related MCP server: Simple Weather MCP
30+ built-in Norwegian cities (Oslo, Bergen, Tromsø, Lofoten, Svalbard...)
Geocoding fallback for any Norwegian location
Coordinate input support (lat,lon)
MET Norway's symbol codes mapped to readable descriptions
{
"mcpServers": {
"norwegian-weather": {
"command": "node",
"args": ["/path/to/mcp-norwegian-weather/src/index.js"]
}
}
}MET Norway Locationforecast 2.0 — free, requires User-Agent identification (included). Powers yr.no, one of the world's most accurate weather services.
This is one of several Nordic MCP servers. See mcp-nordic for the unified meta-server.
MIT
Get current weather for a location in Norway using MET Norway (yr.no).
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | Norwegian city name or lat,lon coordinates |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does (get current weather) but doesn't describe any behavioral traits like rate limits, authentication requirements, error conditions, response format, or data freshness. For a read operation with no annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 core purpose, geographic scope, and data source without any wasted words. It's appropriately front-loaded with the main action and resource. Every element in the sentence serves a clear informational purpose.
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 that there are no annotations and no output schema, the description should provide more complete context about what to expect from this tool. While it clearly states the purpose and geographic limitation, it doesn't describe the response format, potential errors, or any behavioral constraints. For a tool with no structured metadata beyond the input schema, this leaves the agent with insufficient information to use it effectively.
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 schema description coverage is 100% (the 'location' parameter is fully documented in the schema as 'Norwegian city name or lat,lon coordinates'). The description doesn't add any parameter-specific information beyond what's already in the schema. With high schema coverage, the baseline score of 3 is appropriate since the description doesn't compensate with additional parameter context.
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 verb ('Get') and resource ('current weather') with specific geographic scope ('for a location in Norway using MET Norway (yr.no)'). It distinguishes from the sibling 'weather_forecast' by specifying 'current' weather rather than forecast. However, it doesn't explicitly contrast with the sibling tool beyond the temporal difference.
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 when to use this tool (for current weather in Norway) versus the sibling 'weather_forecast' (presumably for future predictions). However, it doesn't provide explicit guidance on when NOT to use it or mention any prerequisites or alternatives beyond the temporal distinction. The geographic limitation is clear but not framed as an exclusion guideline.
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 Norway using MET Norway (yr.no).
| Name | Required | Description | Default |
|---|---|---|---|
| location | Yes | Norwegian 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?
No annotations are provided, so the description carries full burden. It mentions the data source ('MET Norway (yr.no)') which adds useful context about reliability and regional focus. However, it lacks behavioral details like rate limits, authentication needs, error handling, or what the forecast includes (e.g., temperature, precipitation).
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 with zero waste. It front-loads the core purpose ('Get hourly weather forecast') and includes essential context (location scope and data source) without 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 no annotations and no output schema, the description provides basic purpose and scope but lacks details on behavior, return values, or error cases. For a simple read-only tool with full schema coverage, this is minimally adequate but leaves gaps in understanding how the tool behaves in practice.
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 fully documents both parameters. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't clarify format for 'location' or typical values for 'hours'). Baseline 3 is appropriate when schema does the heavy lifting.
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 hourly weather forecast'), resource ('for a location in Norway'), and data source ('using MET Norway (yr.no)'). It distinguishes from the sibling 'current_weather' by specifying 'hourly forecast' versus current conditions. However, it doesn't explicitly contrast with the sibling tool name.
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 context by specifying 'hourly weather forecast' and 'Norway', suggesting this is for future predictions rather than current conditions. However, it doesn't provide explicit guidance on when to use this versus the 'current_weather' sibling tool or any exclusions.
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. Dates show when Glama detected each change.
v0.1.0current_weatherweather_forecastThe two tools have clearly distinct purposes: current_weather retrieves current conditions, while weather_forecast provides hourly forecasts. There is no overlap or ambiguity between them, making it easy for an agent to select the appropriate tool based on the need for immediate vs. future weather data.
Both tools follow a consistent snake_case naming pattern with a clear 'noun_adjective' structure (current_weather, weather_forecast). The naming is predictable and readable, with no deviations or mixed conventions.
With only 2 tools, the server feels thin for a weather domain, as it lacks capabilities like historical data, alerts, or multi-day forecasts. While the tools cover basic current and forecast needs, the count is borderline minimal for comprehensive weather functionality.
The server provides core current and forecast operations but has notable gaps, such as missing historical weather data, severe weather alerts, or location search tools. Agents can work around this for basic queries, but the surface is incomplete for full weather domain coverage.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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An MCP server for weather information by @kulybaba
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