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Nebenkosten einer Stadt

get_city_costs
Read-onlyIdempotent

Nebenkosten-Kennzahlen einer der 400 größten deutschen Städte: Grundsteuer-Hebesatz, Frischwasser- und Abwasserpreis, Müllgebühren sowie Vergleichswerte für Heizung, Versicherung, Gartenpflege und Hauswart. Jeder Wert mit Datenstand und Datenqualität (belegt, Modellwert oder Richtwert). (EN: Utility and service charges (Nebenkosten) for the 400 largest German cities: property tax rate, water, sewage, waste fees and benchmarks for heating, insurance, gardening and caretaker, each with data date and quality.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesStadtname oder Slug, z. B. 'München', 'frankfurt-am-main'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so safety is covered. The description adds genuine behavioral context beyond that: each value carries a Datenstand (data date) and Datenqualität tier (belegt / Modellwert / Richtwert), which tells the agent how much to trust individual figures.

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?

Front-loaded with the resource and scope, then the returned fields, then the data-quality caveat. The German/English duplication adds length but serves discoverability; overall the text is dense with content rather than filler.

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?

With no output schema and full annotation coverage, the description carries the return-value burden itself — and it does so by listing the exact metrics and the data-quality metadata. The one noticeable gap is the unaddressed overlap with the property-tax sibling tool.

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

Parameters3/5

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

Schema description coverage is 100% for the single city parameter, which already documents 'Stadtname oder Slug' with examples. The description adds no further parameter syntax or formatting guidance, so the schema does the heavy lifting and a baseline 3 is appropriate.

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 resource (Nebenkosten/utility cost metrics) and enumerates exactly which values are returned (Grundsteuer-Hebesatz, water, sewage, waste fees, plus benchmarks), plus the addressable scope (400 largest German cities). It does not, however, differentiate itself from the sibling get_property_tax_rate, whose subject matter clearly overlaps with the Grundsteuer-Hebesatz field.

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?

There is no explicit when-to-use guidance, no statement of prerequisites, and no mention of the sibling get_property_tax_rate or get_housing_benefit_rent_level as alternatives. Usage is only implied by the resource name.

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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