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meteo-swiss-mcp

by cuolm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

pressure_msl

Get sea-level pressure in Pascals for a Swiss location at a specific hour offset from today 00:00 local time.

Instructions

        Get sea-level pressure 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: Sea-level pressure in Pascals (Pa).

        Examples:
            pressure_msl("Zurich", 2)    # Pressure at 02:00 Swiss local time today
            pressure_msl("Zurich", 14)   # Pressure at 14:00 Swiss local time today
            pressure_msl("Zurich", 36)   # Pressure at 12:00 Swiss local time tomorrow
            pressure_msl("Zurich", 113)  # Pressure 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

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses non-obvious constraints: lead_time_swiss is an hour offset from 00:00 Swiss local time, min/max valid values are 2 and 121 hours, and the return value is in Pascals. This is valuable context beyond a simple 'get pressure' statement.

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 description is front-loaded with purpose and then compactly documents parameters, return type, and examples. Every part earns its place, especially the examples that disambiguate the offset semantics.

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 two-parameter read tool, the description is complete: both parameters are fully specified, return units are stated, and examples cover multiple offset ranges. The output schema covers the return shape, so nothing essential is missing for correct invocation.

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 coverage is 0%, so the description must compensate, and it does thoroughly. location is explained with an example, and lead_time_swiss gets a precise meaning, valid range, and illustrative examples mapping offsets to Swiss local times.

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 uses a specific verb and resource: "Get sea-level pressure for a location at a specific offset time." This clearly differentiates it from sibling weather-variable tools like temperature, wind_speed, or total_rainfall, even without naming them.

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 intended purpose is clear enough to infer when to use the tool, but there is no explicit 'use this when' guidance or mention of alternatives/exclusions. The agent must infer selection from the purpose statement and sibling tool names.

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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