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meteo-swiss-mcp

by cuolm
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Python
Remote

total_cloud_cover

Get estimated total cloud cover in percent for any Swiss location and time. Also returns low, medium, and high cloud layers to distinguish fog from high cloud.

Instructions

        Get how cloudy it is at a location at a specific time.

        MeteoSwiss publishes low, medium and high cloud separately, and they overlap, so the
        total is an estimate that assumes the layers are independent. All three layers are
        returned as well, which tells low fog apart from thin high cloud.

        When a number is not needed, hourly_forecast answers "how cloudy" in plain words.

        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: Estimated total cloud cover in percent, the low, medium and high layers in
                percent, the resolved location with its altitude, the time it is valid for,
                and the model run.

        Examples:
            total_cloud_cover("Zurich", "2026-09-23T14:00")
            total_cloud_cover("Locarno", "2026-09-24T09:00")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whenYes
locationYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.2.0
    • removedInput schema / properties / lead_time_swiss
      Removed value: -{
-  "title": "Lead Time Swiss",
-  "type": "integer"
-}
    • addedInput schema / properties / when
      Added value: +{
+  "title": "When",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "location",
-  "lead_time_swiss"
-]New value: +[
+  "location",
+  "when"
+]
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "title": "Result",
-      "type": "number"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "total_cloud_coverOutput",
-  "type": "object"
-}New value: +null
  2. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the total is an estimate assuming independent layers, and explains that all three layers are returned to distinguish low fog from thin high cloud. It also describes the return dict contents (location, altitude, valid time, model run), offering good behavioral transparency beyond the basic purpose.

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 a clear opening, an explanatory note about the estimation, explicit Args/Returns/Examples sections. It is slightly longer than necessary but each sentence provides value, including the estimation nuance and the alternative routing. The structure is front-loaded with the core purpose, making it easy to scan.

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 no output schema and no annotations, the description covers everything an agent needs: parameter format and examples, the return value structure, the estimation logic, and the differentiation from a sibling. It even includes usage examples. Nothing is missing for correct invocation and interpretation of results.

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 fully explain the parameters. It does: location is defined as a location name or Swiss postal code with examples, and when is defined as Swiss local time in ISO 8601 without offset, with an example and a valid range (today to 8 days ahead). This completely compensates for the lack of schema descriptions.

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 states a specific verb and resource: 'Get how cloudy it is at a location at a specific time.' It clearly distinguishes itself from the sibling hourly_forecast by explaining that this tool returns a numeric total and layer details, whereas hourly_forecast answers in plain words. The purpose is unambiguous and differentiates from siblings.

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 provides a clear alternative: 'When a number is not needed, hourly_forecast answers "how cloudy" in plain words.' This implies that this tool is for numeric cloud cover data. While it doesn't explicitly enumerate all when-not scenarios, it gives a concrete condition for choosing the alternative, which is sufficient guidance.

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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