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schlpbch

Aareguru MCP Server

by schlpbch
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Forecast View

forecast_view

Get a 24-hour forecast with air-temperature chart, hourly card strip, and 2-hour water temperature trend, plus MeteoSwiss weather symbols and precipitation for the full day.

Instructions

Show a 24-hour forecast with air-temperature chart and hourly card strip.

Displays the 2-hour water temperature trend alongside an hourly weather prognosis (MeteoSwiss symbols, air temp, precipitation) for the full day.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoCity identifier (e.g. 'Bern', 'Thun', 'Olten')Bern
langNode

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv4.9.0
    • addedInput schema / properties / lang
      Added value: +{
+  "default": "de",
+  "type": "string"
+}
  2. Addedv4.6.0

TDQS

B3.1/5.0
Behavior3/5

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

No annotations are present, so the description itself must convey behavior. 'Show' and 'Displays' signal a read-only rendering operation, and it discloses the data source (MeteoSwiss symbols) and chart/card composition. However, it does not mention side effects, data freshness, or error/empty-city behavior; for a view this is a modest rather than severe gap.

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?

Two short sentences, each adding distinct content: the first states the core deliverable, the second adds the water-temperature and weather-prognosis details. There is no filler or repetition of the title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description captures the main visual components and data source, which is good for a simple view with no output schema. It falls short on lang semantics and on when to use this tool versus sibling forecast/refresh tools, so an agent has to infer part of the calling context.

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

Parameters2/5

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

City is documented in the schema with examples, but lang is not described anywhere, and the description does not mention either parameter or how they affect the forecast. With only 50% schema coverage, the description fails to compensate for the undocumented lang parameter.

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 opens with 'Show a 24-hour forecast with air-temperature chart and hourly card strip', which names a specific action and deliverable. It further details the water-temperature trend and MeteoSwiss weather prognosis, making it clear this is a rendered view rather than a data-fetch endpoint. It does not explicitly contrast with siblings like get_forecasts or refresh_forecast, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No sentence addresses when to invoke this tool versus get_forecasts, refresh_forecast, or current-condition views. The description only enumerates displayed content, leaving the agent to infer that it is for presenting forecast UI. There are no exclusions or alternative routing cues.

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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