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MET Norway — 9-Day Hourly Weather Forecast

metno.weather.forecast
Read-onlyIdempotent

Get a 9-day (216-hour) hourly weather forecast for any global lat/lon point from the Norwegian Meteorological Institute Locationforecast 2.0 model. Returns air temperature, wind speed/direction/gusts, cloud cover, humidity, pressure, UV index, precipitation amounts and probability, and symbolic weather codes (e.g. clearsky_day, rain). Global coverage. CC BY 4.0 + NLOD 2.0, no auth, no upstream cost.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude in decimal degrees (e.g. 59.91 for Oslo, -33.87 for Sydney)
lonYesLongitude in decimal degrees (e.g. 10.75 for Oslo, 151.21 for Sydney)
hoursNoNumber of hourly forecast entries to return (1–216, default 48). Full 9-day range = 216.
altitudeNoStation altitude in metres above sea level. Improves pressure correction.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description adds useful behavioral context: the exact data fields returned, the licensing terms (CC BY 4.0 + NLOD 2.0), and the fact that no authentication or upstream cost is required. It does not contradict annotations and goes beyond what they already declare.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core action and resource, followed by a compact list of return fields and then licensing/coverage facts. Every phrase adds value, and it remains concise despite covering many aspects.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema, the description doesn't need to explain return formatting. It covers what the tool does, what data it returns, global coverage, licensing, and auth requirements. The annotations handle safety and idempotency. Nothing critical is missing for an agent to correctly invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers 100% of the parameters with descriptions and ranges. The tool description does not add extra meaning beyond what the schema provides; it only reiterates the 216-hour/9-day duration, which mirrors the 'hours' parameter. Since the schema is complete, a score of 3 is appropriate per the baseline rule.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Get') and a precise resource: a 9-day (216-hour) hourly weather forecast for any global lat/lon point from the Norwegian Meteorological Institute Locationforecast 2.0 model. It also lists the returned data fields (temperature, wind, cloud cover, humidity, pressure, UV index, precipitation, weather codes), which disambiguates it from the many other weather tools in the sibling list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context on when to use this tool: it works for 'any global lat/lon point' and explicitly states 'Global coverage' and 'no auth, no upstream cost.' This helps an agent decide to use it for global, free, and immediate forecast needsency, though it does not list exclusions or name alternative tools for region-specific forecasts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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