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meteo-swiss-mcp

by cuolm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

freezing_level

Get the altitude where air reaches 0°C at a Swiss location and time, so you can determine how high a ski area must be for snow.

Instructions

        Get the height of the 0 degree line at a location at a specific time.

        This is the height where the air is at 0 degrees. Snow usually reaches somewhat below it,
        so it answers questions about snow in the mountains, such as how high a ski area has to be.

        Args:
            location (str): Location name (e.g., "Zurich") or Swiss postal code (e.g., "8001").
            when (str): Swiss local time in ISO 8601 without offset, e.g. "2026-09-23T14:00". Today or up to 8 days ahead.

        Returns:
            dict: The freezing level in metres above sea level, the resolved location with its
                altitude, the time it is valid for, and the model run.

        Examples:
            freezing_level("Zermatt", "2026-09-23T14:00")
            freezing_level("Davos", "2026-09-25T06:00")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whenYes
locationYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It explains the output concept (height where air is 0 degrees), the relationship to snow, the required time format and range, and the return dict contents. It does not disclose edge cases or data sources, but it is transparent about the core behavior and result.

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 concise yet complete: one opening sentence, a clarifying context sentence, then clear Args/Returns/Examples sections. Every sentence earns its place, and information is front-loaded with the core purpose before details.

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 two required parameters, no output schema, and no annotations, the description provides everything an agent needs: parameter formats, time constraints, return value structure, and usage examples. It is fully complete for correct invocation.

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%, but the description fully compensates. It defines location as a name or Swiss postal code with examples, and when as Swiss local time in ISO 8601 without offset with a concrete example and validity window. This adds all necessary meaning beyond the bare parameter names.

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 a specific verb and resource: 'Get the height of the 0 degree line at a location at a specific time.' It clearly differentiates from weather siblings like hourly_forecast or rain_outlook by focusing on the freezing level, and the snow-context sentence reinforces 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 Guidelines4/5

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

The description explains when this tool is relevant: 'Snow usually reaches somewhat below it, so it answers questions about snow in the mountains.' It does not explicitly name alternative tools or say when not to use it, but the context is clear enough for an agent to select it appropriately.

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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