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meteo-swiss-mcp

by cuolm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

total_rainfall

Calculate total rainfall in millimeters for a Swiss location over a specified hour range, from 2 to 121 hours ahead.

Instructions

        Get total rainfall for a location and offset period.

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

        Returns:
            float: Total precipitation accumulation in millimeters for the given period.

        Examples:
            total_rainfall("Zurich", 2, 24)    # Total rainfall today
            total_rainfall("Zurich", 24, 48)   # Total rainfall tomorrow
            total_rainfall("Zurich", 24, 30)   # Total rainfall tonight
            total_rainfall("Zurich", 30, 36)   # Total rainfall tomorrow morning
            total_rainfall("Zurich", 36, 42)   # Total rainfall tomorrow afternoon
            total_rainfall("Zurich", 42, 48)   # Total rainfall tomorrow evening

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationYes
lead_time_end_swissYes
lead_time_start_swissYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden and does a reasonably good job: it states valid offset bounds (minimum 2 hours, maximum 121 hours), clarifies the timezone basis ('Swiss local time'), and specifies the return type and unit (float in millimeters). It does not discuss error behavior or data availability, which prevents a higher score.

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 well-structured with clear Args, Returns, and Examples sections, and the core purpose is front-loaded. The six examples are somewhat repetitive but collectively illustrate the meaning of the offset parameters effectively.

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?

This is a simple three-parameter tool with no nested objects or enum constraints, and the description covers all parameters, valid ranges, return units, and illustrative time-window examples. An agent has enough information to call the tool correctly without additional context.

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 description coverage is 0%, so the description must supply all semantic meaning. It explains 'location' with an example, and defines both lead_time_start_swiss and lead_time_end_swiss as hour offsets from today at 00:00 Swiss local time with min/max constraints. The examples further clarify the expected parameter relationships.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the action ('Get total rainfall'), the resource (a location and offset period), and the return unit (millimeters). It is easily understandable and distinct enough from siblings like precipitation_rate, though it does not explicitly call out a sibling for differentiation.

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 examples imply usage scenarios such as 'total rainfall today' and 'tomorrow morning', which help an agent understand the intended time-window semantics. However, there is no explicit guidance on when to prefer this tool over alternatives like precipitation_rate or total_cloud_cover.

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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