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Bigred97

au-weather-mcp

get_weather

Query Australian weather for any date range, automatically fetching historical data or forecasts. Returns daily aggregates or hourly observations.

Instructions

Query weather over a date range. Routes to historical archive or forecast endpoint automatically based on the date range.

Routing logic: - end_date in the past (>= 5 days ago) → historical archive (1940+) - start_date in the future → forecast (today + 16 days max) - range straddles today → forecast with past_days set

Examples: # Historical: how was Sydney summer 2020? resp = await get_weather( "sydney", start_date="2020-01-01", end_date="2020-01-31", granularity="daily", ) # → 31 DailyAggregate rows with temp_max, temp_min, precip per day

# 7-day forecast for Melbourne, hourly detail
resp = await get_weather(
    "melbourne",
    start_date="2026-05-12",
    end_date="2026-05-19",
    granularity="hourly",
)
# → 168 hourly WeatherObservation rows

# Just today (omit both dates)
resp = await get_weather("brisbane")
# → today's daily aggregate

When to use: - Time-series queries (forecast over the next week, or historical comparison) - Multi-day weather analysis - Climate research and historical look-backs (decade-scale via archive)

Returns: WeatherResponse with either daily or hourly populated depending on granularity. Period bounds populated from actual returned data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationYesAny Australian location. Same accepted shapes as latest(): curated ID, place name (any case), state code/name, or raw 'lat,lng' coordinates.
start_dateNoInclusive start date in 'YYYY-MM-DD' format. Open-Meteo's historical archive covers 1940-01-01 onwards (5-day lag). Forecast covers today through today + 16 days.
end_dateNoInclusive end date in 'YYYY-MM-DD' format. Same range rules as start_date.
granularityNoTime resolution of the returned series. 'daily' (default) returns one row per day with max/min/sum aggregates. 'hourly' returns one row per hour with point observations — useful for intraday detail but expect ~24× more records.daily

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
location_idYes
location_nameYes
stateYes
latitudeYes
longitudeYes
timezoneYes
location_resolutionYes
location_inputYes
queryNo
periodNo
currentNo
hourlyNo
dailyNo
sourceNoOpen-Meteo (aggregates Bureau of Meteorology data under licence)
attributionNoWeather data by Open-Meteo.com (https://open-meteo.com), licensed under CC BY 4.0. Underlying data includes the Australian Bureau of Meteorology (https://www.bom.gov.au) under Open-Meteo's licensing arrangement.
source_urlYes
retrieved_atYes
server_versionYes
staleNo
stale_reasonNo
Behavior5/5

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

With no annotations, the description fully discloses behavior: routing based on dates, API source (Open-Meteo), date constraints (5-day lag, 16-day forecast limit), and return format (WeatherResponse with daily/hourly). No contradictions.

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 structured with sections (routing, examples, when to use, returns) and is front-loaded with the core purpose. While thorough, it could be slightly tightened without losing clarity.

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

Completeness4/5

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

The description covers all essential aspects: purpose, routing, constraints, examples, and return format. Assuming an output schema exists (mentioned), it is well-rounded for a tool with moderate complexity.

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

Parameters4/5

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

Schema coverage is 100% with detailed descriptions, so baseline is 3. The description adds value through examples showing usage patterns and routing nuances, justifying a higher score.

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 'Query weather over a date range' with specific routing logic to historical or forecast endpoints. It distinguishes itself from sibling tools (e.g., latest for current, describe_location for metadata) by focusing on time-series data over a range.

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?

A dedicated 'When to use' section lists appropriate scenarios like multi-day analysis and climate research. Routing logic clarifies auto-selection between endpoints, but lacks explicit when-not-to-use or direct comparisons to alternatives.

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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