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Xearno Tools

Germany Elterngeld Calculator (Basiselterngeld & ElterngeldPlus)

germany_elterngeld
Read-only

Your monthly Elterngeld under current BEEG law — the exact three-segment replacement rate, the €300–€1,800 clamp, and the cohort income caps (€175k / €200k / €300k) that changed twice in twelve months. Computes German parental allowance (Elterngeld): the eligibility income cap that depends on your child’s birth date (€175,000 for births from 1 April 2025; €200,000 for the year before; €300,000/€250,000 earlier — general AI quotes stale caps or invents a €150,000 single cap that has never existed), the exact BEEG §2 sliding replacement rate (67% only between €1,000–1,200 net — 65% above €1,240, up to 100% at low incomes), the €300–€1,800 Basiselterngeld clamp unchanged since 2007, ElterngeldPlus (half the amount, double the months), Geschwisterbonus, and Mehrlingszuschlag for multiples.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
variantNoWhich variant? ElterngeldPlus pays half the Basis amount for double the months — designed for working part-time while receiving. Both are shown; this picks the headline.basis
householdNoHousehold Only the pre-Apr-2024 cohort has different caps by household type. Single parents can claim all partner months themselves.couple
multiplesNoChildren in this birth Twins = 2. Mehrlingszuschlag adds €300 per additional child of the same birth (€150 in Plus months).
monthlyNetNoYour average monthly net earned income before birth (€/mo) Average monthly NET earned income of the 12 months before birth (before Mutterschutz for the mother). Months on Elterngeld for an older child, on Mutterschaftsgeld, or ill due to pregnancy are skipped — the window reaches further back instead.
birthCohortNoWhen is (was) your child born? The decisive input: the eligibility income CAP depends on the child’s birth date — €175k / €200k / €300k(couples)-€250k(singles). This cohort trap is what general AI misses entirely (it also invents a €150k single cap that has never existed).from2025
siblingBonusNoGeschwisterbonus (sibling bonus)? Applies while at least one other child under 3 (or two others under 6) lives in the household: +10% of your Elterngeld, minimum €75 (€37.50 in Plus months).no
taxableIncomeNoTaxable income in the calendar year before birth (zu versteuerndes Einkommen) (€/yr) The household’s zu versteuerndes Einkommen per the Steuerbescheid — taxable income, NOT gross salary — in the calendar year before the birth. Over the cohort’s cap → no Elterngeld at all.

TDQS

A4.7/5.0
Behavior5/5

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

Detailed description of calculation logic, cohort-dependent caps, sliding replacement rates, and bonuses. ReadOnlyHint is consistent; no contradictions. Adds significant behavioral context beyond annotations.

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?

Well-structured with a clear opening sentence, but the description is quite long. Could be slightly more concise, but the detail is justified by complexity.

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?

Covers all relevant aspects: eligibility, calculation rules, cohorts, bonuses, warnings about common mistakes. No output schema, but description is self-contained and complete.

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?

Adds substantial meaning beyond the input schema by explaining the logic behind each parameter (e.g., cohort trap, income definitions). Schema coverage is 100%, but description enriches understanding.

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?

Clearly states it computes monthly Elterngeld under BEEG law with specific details on replacement rates, caps, and bonuses. Distinct from sibling tools, which are other calculators.

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?

Provides clear context on when to use (accurate German parental allowance calculation) and warns about common AI errors. No explicit when-not-to-use, but sibling differentiation is clear.

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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TDQS

A3.9/5.0
Disambiguation4/5

Each tool targets a distinct niche (e.g., specific country tax rules, loan types, or legal calculations), with detailed descriptions that clarify boundaries. However, the large number of tools (66) could cause some confusion for an agent trying to select the right one for a general query, especially when multiple tools relate to the same country.

Naming Consistency4/5

Tool names follow a mostly predictable pattern: lowercase words separated by underscores, often starting with a country name (e.g., 'uk_stamp_duty_sdlt') or a topic (e.g., 'compound_growth'). There are minor deviations, such as abbreviations ('npv_irr', 'sip') and varying use of verbs, but overall the naming is clear and consistent.

Tool Count3/5

At 66 tools, the server is unusually large and covers an extensive range of financial and legal calculators. While each tool justifies its existence, the count exceeds the typical well-scoped range (3–15), making the server feel bloated. A more modular design might improve coherence.

Completeness4/5

The tool set covers a wide array of domains: personal income taxes, property taxes, loan calculations, investment returns, and specific country regulations. Minor gaps exist (e.g., missing tools for corporate taxes, general retirement planning, or insurance), but the overall coverage is thorough and addresses many niche scenarios that general AI handles poorly.

Resources