Skip to main content
Glama
Sign In
cuolm

meteo-swiss-mcp

by cuolm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

forecast_sunshine

Get the sunshine hours for a place over a period.

Instructions

        Get the sunshine hours for a place over a period.

        MeteoSwiss publishes sunshine as minutes per hour, which are added up over the period
        and reported as hours.

        Args:
            location (str): Place name: a town, village, city district, lake or peak (e.g., "Zurich").
            start (str): Swiss local time the period starts, as a full hour in ISO 8601 without offset, e.g. "2026-09-23T06:00". Today or up to 8 days ahead.
            end (str): Swiss local time the period ends, as a full hour in ISO 8601 without offset, e.g. "2026-09-23T18:00".

        Returns:
            dict: The sunshine in hours, the place, the forecast point used, the period and the
                model run.

        Examples:
            forecast_sunshine("Zurich", "2026-09-23T00:00", "2026-09-24T00:00")   # the whole day
            forecast_sunshine("Zurich", "2026-09-23T12:00", "2026-09-23T18:00")   # the afternoon

Input Schema

NameRequiredDescriptionDefault
endYes
nearNoOnly when several places share the name: a village next to the place the user means. For "the Blausee near Bettmeralp" use location "Blausee" and near "Bettmeralp". Leave it out otherwise.
startYes
locationYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does well: it explains the MeteoSwiss minutes-per-hour aggregation, gives the allowed forecast horizon, and lists what the return value contains. It stops short of mentioning error modes or ambiguity handling for place names, which the optional near parameter addresses in the schema.

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 the core purpose and then organized into Args, Returns, and Examples. It is slightly long for a simple forecast query, but every section adds useful information rather than repeating structured fields.

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?

Given no output schema and no annotations, the description sufficiently covers inputs, return contents, aggregation behavior, and date constraints. It leaves minor gaps around handling ambiguous locations and sibling-tool selection, but an agent has enough to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 25%, so the description compensates by explaining location, start, and end semantics, including ISO 8601 format without offset, Swiss local time, full-hour requirement, and date range. The optional near parameter is omitted from the description, though the schema itself documents it.

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 states a specific verb and resource: get sunshine hours for a place over a period. The metric is distinct from sibling forecast tools like rain, wind, and cloud cover, though it does not explicitly name or differentiate those alternatives.

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?

It gives useful examples for whole-day and afternoon queries and notes the forecast window is today or up to 8 days ahead. However, it does not say when to use this tool versus forecast_daily, forecast_hourly, or other sibling tools, so guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools