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IBM

chuk-mcp-open-meteo

by IBM

interpret_weather_code

Interpret WMO weather codes (0-99) into human-readable descriptions with severity and icon.

Instructions

Interpret WMO weather codes used by Open-Meteo API.

Weather codes are numerical values (0-99) that represent different weather conditions. This tool translates codes into human-readable descriptions.

Args: weather_code: WMO weather code integer (0-99) from weather forecast data. This is the 'weathercode' field in current_weather or hourly/daily data.

Returns: WeatherCodeInterpretation: Pydantic model with: - code: The weather code number - description: Human-readable weather condition - severity: Category (clear, cloudy, fog, drizzle, rain, freezing, snow, showers, thunderstorm)

Common Weather Codes: Clear/Cloudy (0-3): 0 = Clear sky 1 = Mainly clear 2 = Partly cloudy 3 = Overcast

Fog (45-48):
    45 = Fog
    48 = Depositing rime fog

Drizzle (51-57):
    51 = Light drizzle
    53 = Moderate drizzle
    55 = Dense drizzle
    56-57 = Freezing drizzle

Rain (61-67):
    61 = Slight rain
    63 = Moderate rain
    65 = Heavy rain
    66-67 = Freezing rain

Snow (71-77, 85-86):
    71 = Slight snow
    73 = Moderate snow
    75 = Heavy snow
    77 = Snow grains
    85-86 = Snow showers

Showers (80-82):
    80 = Slight rain showers
    81 = Moderate rain showers
    82 = Violent rain showers

Thunderstorm (95-99):
    95 = Thunderstorm
    96 = Thunderstorm with slight hail
    99 = Thunderstorm with heavy hail

Tips for LLMs: - Use this to explain weather conditions to users in natural language - Severity helps determine appropriate activity recommendations - Codes 0-3: Generally safe outdoor conditions - Codes 51-65: Wet conditions, bring umbrella - Codes 71-77: Snow conditions, winter gear needed - Codes 80-99: Severe weather, take precautions - Unknown codes return "Unknown weather code" - may be API error

Map icon tip: The returned icon field is a PNG URL for the weather condition. When building a GeoJSON FeatureCollection to show on a map, put this URL in each feature's properties.icon field — the map renderer will use it as the marker image instead of the default blue pin.

Example: # Get weather and interpret code forecast = await get_weather_forecast(lat, lon, current_weather=True) code = forecast.current_weather.weathercode interpretation = await interpret_weather_code(code) # Returns: WeatherCodeInterpretation(code=61, description="Slight rain", # severity="rain", icon="https://openweathermap.org/img/wn/10d@2x.png")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
weather_codeYes
Behavior5/5

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

With no annotations, the description fully covers behavioral details: input range (0-99), return model fields (code, description, severity, icon), error handling for unknown codes, and even map icon usage. This exceeds typical descriptions.

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

Conciseness4/5

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

The description is fairly long but well-structured with clear sections (Args, Returns, Common Codes, Tips, Example). Every section adds value, though some details (like map icon tip) could be condensed if brevity were prioritized.

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 tool has one parameter, no output schema, and no annotations, the description is remarkably complete: it explains the input, output, error cases, common codes, and practical usage tips, leaving no gaps.

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

Parameters5/5

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

The schema only defines weather_code as an integer, with 0% description coverage. The description adds full semantics: it's the 'weathercode' field from weather data, range 0-99, and includes a detailed list of common codes with their meanings.

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 clearly states it interprets WMO weather codes (0-99) into human-readable descriptions, using a specific verb and resource. It distinguishes from sibling tools which fetch data (e.g., get_weather_forecast, geocode_location) rather than interpret codes.

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?

Provides tips for when to use the tool, such as explaining weather conditions and recommending activities based on severity. However, does not explicitly state when not to use it or name alternative tools for different needs.

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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