Skip to main content
Glama
slettmayer

geosphere-mcp-server

by slettmayer

get_storm_outlook

Get a severe-weather outlook for a location, including peak wind gusts, thunderstorm timing, and CAPE. Covers Austria and the Alpine region.

Instructions

Get the severe-weather outlook for a location: gusts and thunderstorms.

Reports the peak wind gust within the next hour and the next 12 hours,
whether a thunderstorm is expected within the next hour, when the next
thunderstorm is expected across the whole forecast horizon, and the peak
CAPE over the next 12 hours. Deliberately reports no severity verdict —
what counts as dangerous is the caller's judgement.

High-resolution GeoSphere AROME data, whose CAPE is gated by convective
inhibition. Serves Austria and the Alpine region only; a point outside that
grid returns an out-of-coverage notice.

The thunderstorm scan covers the AROME horizon, nominally ~60 h but shorter
when a run is stale or truncated. A "none in the next N h" answer names the
horizon it actually covered; it is not an all-clear beyond that.

Horizons round up to whole hours: the "next hour" window covers the hour
already under way plus the next one. A thunderstorm timestamp at or before
the current time means one is already in progress.

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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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 provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It explains the data source (GeoSphere AROME), the CAPE gating by convective inhibition, the thunderstorm scan horizon and its caveats, the rounding of time windows, and the meaning of timestamps for ongoing thunderstorms.

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 moderately long but every sentence adds unique value. It is structured with a clear opening, a list of returned metrics, data-source context, coverage caveats, and parameter details. No fluff or repetition.

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?

Despite the tool's complexity, the description covers the returned values, edge cases (stale or truncated runs, out-of-coverage notices), and interpretation of results. An output schema is present, but the description goes beyond it to explain behavioral nuances, making it complete for an agent.

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 input schema provides no descriptions for latitude and longitude (0% coverage), but the description fully compensates by giving decimal examples ('48.2208 for Vienna') and instructing the user to geocode city names to coordinates themselves. This adds clear meaning 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 description opens with 'Get the severe-weather outlook for a location: gusts and thunderstorms', which names a specific verb (get), a resource (severe-weather outlook), and the content (gusts and thunderstorms). This clearly distinguishes it from sibling tools like get_current_weather and get_hourly_forecast.

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 provides clear context for when to use the tool, such as the geographic scope (Austria and the Alpine region) and the note that it deliberately reports no severity verdict, leaving judgment to the caller. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of a full 5.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/slettmayer/geosphere-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server