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Bigred97

au-weather-mcp

get_weather

Retrieve historical and forecast weather for any Australian location over a specified date range, with daily or hourly granularity. Automatically selects archive or forecast based on the dates provided.

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
end_dateNoInclusive end date in 'YYYY-MM-DD' format. Same range rules as start_date.
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.
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
dailyNo
queryNo
staleNo
stateYes
hourlyNo
periodNo
sourceNoOpen-Meteo (aggregates Bureau of Meteorology data under licence)
currentNo
latitudeYes
timezoneYes
longitudeYes
source_urlYes
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.
location_idYes
retrieved_atYes
stale_reasonNo
location_nameYes
location_inputYes
server_versionYes
location_resolutionYes
Behavior4/5

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

With no annotations, the description fully discloses routing logic, date constraints (1940+ lag, 16-day forecast), and default behavior when dates are omitted. It does not cover rate limits or permissions, but provides enough detail for safe invocation.

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 well-structured with clear sections (routing logic, examples, when to use, returns). It is relatively lengthy due to examples, but front-loaded with purpose. Each part serves a purpose, though some repetition could be trimmed.

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?

The description is comprehensive for a tool with routing logic and date complexity. It covers all key behaviors, provides examples for different scenarios, and explains the return type. With an output schema existing, further detail is not needed.

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%, but the description adds significant value by explaining automatic routing based on date values, showing example calls, and clarifying granularity impact (e.g., ~24x more records for hourly). This exceeds the baseline of 3.

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 the tool queries weather over a date range and distinguishes it from sibling tools like 'latest' which handles current conditions. It specifies automatic routing between historical and forecast endpoints.

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 'When to use' section explicitly lists three use cases (time-series queries, multi-day analysis, climate research), but does not mention when to avoid using it or suggest alternatives like 'latest' for current conditions.

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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