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slettmayer

geosphere-mcp-server

by slettmayer

get_hourly_forecast

Retrieve hour-by-hour weather forecasts for any location, with high-resolution GeoSphere data in Austria/Alps and Open-Meteo fallback elsewhere.

Instructions

Get an hour-by-hour weather forecast for a location.

High-resolution GeoSphere AROME data (up to ~60 h, with C-LAEF
precipitation probability) is used inside its Austria/Alps coverage;
elsewhere the tool falls back to Open-Meteo (up to 48 h). The response
states which source served it.

Args:
    latitude: Decimal latitude (e.g. 48.2208 for Vienna). Geocode city
        names to coordinates yourself.
    longitude: Decimal longitude (e.g. 16.3738 for Vienna).
    hours: Number of forecast hours (default 24, clamped to 1–60 on
        GeoSphere / 1–48 on the Open-Meteo fallback).
    start: Optional ISO 8601 start time (e.g. "2026-07-22T15:00"); the
        forecast begins at/after this instant instead of now.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNo
startNo
latitudeYes
longitudeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description carries the full burden and discloses the data-source selection (GeoSphere AROME vs Open-Meteo), forecast time limits, hour clamping behavior, and the fact that the response indicates its source. It also explains start-time semantics, providing comprehensive behavioral context.

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 well structured with a clear purpose statement, a data-source paragraph, and a detailed Args list. Every sentence provides necessary information, and the layout makes the content easy to scan. Front-loaded with the core purpose.

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 tool has an output schema (so response format is covered), and the description covers all operationally relevant aspects: coverage area, fallback, time limits, clamping, start time, and parameter units. Nothing is left unclear for an agent to invoke it correctly.

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?

Schema coverage is 0% (no parameter descriptions in the schema). The description compensates fully by explaining each parameter with examples (lat/long for Vienna), default and clamping behavior for 'hours', and the ISO 8601 format and semantics for 'start'. This adds meaning far beyond the bare schema.

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 opening sentence 'Get an hour-by-hour weather forecast for a location' uses a specific verb and resource, clearly distinguishing it from sibling tools get_current_weather and get_daily_forecast. The description further specifies the data sources and coverage areas, leaving no ambiguity about its purpose.

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

Usage Guidelines3/5

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

The description implies usage for hourly forecasting but does not explicitly contrast with sibling tools like get_daily_forecast or get_current_weather. It provides clear context about behavior (data sources, fallback) but lacks explicit 'when to use vs. alternatives' guidance.

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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