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cuolm

meteo-swiss-mcp

by cuolm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

current_extremes

Get where in Switzerland it is warmest, coldest, windiest, wettest or sunniest now.

Instructions

        Get where in Switzerland it is warmest, coldest, windiest, wettest or sunniest now.

        This compares the latest measurements of about 160 MeteoSwiss stations. They are the
        values of stations, not of towns, and not a forecast. A high mountain station is
        colder and windier than the valleys, so name the altitude with a station. MeteoSwiss
        publishes new values about every 10 minutes.

        The warmest, coldest and windiest stations are ranked by the temperature and by the
        strongest gust. For the sunniest and the wettest the answer counts stations: a station
        is sunny when the sun shone for at least 5 of the last 10 minutes, and rainy when it
        measured rain in the last 10 minutes. The counts are given for Switzerland and for
        each canton, as the number of sunny or rainy stations out of the stations that measure
        it. Always give both numbers, because a canton with one station says little. A canton
        with 0 sunny or rainy stations has none. A canton that is not listed has no station
        that measures it. Liechtenstein is listed like a canton. At night no station is sunny.

        Args:
            extreme (str): One of "warmest", "coldest", "windiest", "wettest" or "sunniest".

        Returns:
            dict: The time of the measurements and the first 5 stations, or for the sunniest and
                the wettest the counts in all and for each canton, the highest share first; the
                field names give the units. The wettest also lists the first 5 rainy stations.
                A station that does not measure the value is left out everywhere.

        Examples:
            current_extremes("warmest")
            current_extremes("sunniest")

Input Schema

NameRequiredDescriptionDefault
extremeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so in detail: about 160 MeteoSwiss stations, published roughly every 10 minutes, station-level rather than town-level values, not a forecast. It also discloses ranking logic for temperature/gust extremes, count logic for sunny/rainy stations, night behavior, and how missing cantons and non-measuring stations are handled.

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 front-loaded with purpose and then structured around behavior, parameter, return shape, and examples. It is longer than typical but mostly earns its length because the return semantics are complex; a little tightening is possible, but there is no serious redundancy.

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 one required enum parameter, no annotations, and no output schema, the description supplies the missing return-shape detail: measurement time, first five stations, canton counts ordered by highest share, units from field names, and exclusion of non-measuring stations. It is complete enough for an agent to call and interpret the tool without guessing.

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?

Although schema description coverage is 0%, the sole parameter is an enum and the description enumerates all five values while explaining what each choice means. It goes beyond schema by specifying that warmest/coldest/windiest use ranked station values while sunniest/wettest produce station counts plus canton shares.

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 first sentence states a specific verb and scope: get where in Switzerland it is warmest, coldest, windiest, wettest or sunniest now. It also explicitly distinguishes the tool from forecast siblings by saying these are latest measurements and not a forecast, and clarifies station values rather than town values.

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 gives clear usage context: use it for current station extremes, not forecasts, and name altitude when discussing mountain stations. It also explains how to interpret sunny/rainy counts and warns to give both Switzerland and canton numbers. It stops short of explicitly naming sibling alternatives such as current_conditions or forecast_hourly, so it is strong but not fully route-aware.

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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