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meteo-swiss-mcp

by cuolm
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Python
Remote

sunshine_hours

Retrieve predicted sunshine hours for a Swiss location over a specified time window, using hour offsets from today's start.

Instructions

        Get sunshine hours 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: Predicted sunshine hours for the specified period.

        Examples:
            sunshine_hours("Zurich", 2, 24)    # Total sunshine hours today
            sunshine_hours("Zurich", 24, 48)   # Total sunshine hours tomorrow
            sunshine_hours("Zurich", 30, 36)   # Total sunshine hours tomorrow morning
            sunshine_hours("Zurich", 36, 42)   # Total sunshine hours tomorrow afternoon
            sunshine_hours("Zurich", 42, 48)   # Total sunshine hours 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

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full disclosure burden. It conveys a non-mutating read/prediction ('Get ... Predicted sunshine hours'), states the valid offset bounds (2–121 hours), and anchors the time base ('today at 00:00 Swiss local time'). It stops short of describing failure behavior for invalid ranges, so a perfect score is not warranted.

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 docstring is organized into Intro, Args, Returns, and Examples, with the core operation front-loaded. The five examples are slightly redundant but each earns its place by clarifying the offset semantics; no filler is present.

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?

For a tool with three required parameters and no annotation context, the description is complete: it covers every parameter, the return type, the timezone/offset convention, and valid bounds. The output schema exists and the Returns line already states the float result, so nothing an agent needs to call it correctly is missing.

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 parameter meaning, and it does: it defines location with an example, defines both offsets as hours from today 00:00 Swiss time, and gives min/max constraints. The examples additionally map numeric ranges to natural-language periods such as 'today' and 'tomorrow morning.'

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-resource pair ('Get sunshine hours') and immediately scopes it by 'location and offset period.' This target variable is distinct from sibling weather tools such as total_cloud_cover and temperature, and the examples reinforce what the tool is for.

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 Args section and five examples provide clear context for when to call this tool: to obtain forecast sunshine hours over a specific lead-time window, expressed in Swiss local time. It does not explicitly name alternatives or exclusion conditions, but the resource it returns makes the appropriate choice obvious among the weather-variable siblings.

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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