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meteo-swiss-mcp

by cuolm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

wind_speed

Get predicted wind speed for a Swiss location at a specific hour offset from today, enabling informed planning based on forecast conditions.

Instructions

        Get predicted wind speed for a location at a specific offset time.

        Args:
            location (str): Location name (e.g., "Zurich").
            lead_time_swiss (int): Hour offset from today at 00:00 Swiss local time. Min offset time is 2 hours. Max offset time is 121 hours.

        Returns:
            float: Predicted wind speed in meters per second.

        Examples:
            wind_speed("Zurich", 2)    # Wind speed at 02:00 Swiss local time today
            wind_speed("Zurich", 14)   # Wind speed at 14:00 Swiss local time today
            wind_speed("Zurich", 36)   # Wind speed at 12:00 Swiss local time tomorrow
            wind_speed("Zurich", 113)  # Wind speed at 17:00 Swiss local time in 4 days

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationYes
lead_time_swissYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description itself discloses that the value is predicted, constrains lead_time_swiss to 2–121 hours, and specifies output units in meters per second. It does not mention error behavior or data update characteristics, but for a simple read-only query it provides meaningful 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 docstring is organized with Args, Returns, and Examples, front-loading the tool's purpose. Each section adds useful information with no filler, and the examples clarify the offset-to-clock-time mapping efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has only two parameters, both thoroughly explained, and the return type and units are clearly described. It could additionally state what happens for invalid locations or out-of-range offsets, but those details are not required to call the tool 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?

The schema provides only names and types with 0% description coverage, so the description must carry the load. It explains location as a location name and lead_time_swiss as an hour offset from 00:00 Swiss local time, with min/max bounds and worked examples, fully specifying parameter semantics.

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 predicted wind speed for a location at a specific offset time.' This clearly distinguishes it from sibling tools that return other weather variables such as temperature, total_cloud_cover, or precipitation_rate.

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 conveys usage context through the offset-time semantics and examples, but it never explicitly states when to choose this tool over siblings or gives exclusion criteria. Usage is implied rather than directly instructed.

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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