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Rechnungslotse: deutsche E-Rechnung

by de.rechnungslotse

E-Rechnungspflicht klären

pflicht_pruefen
Read-only

Beantwortet für einen konkreten deutschen Betrieb, ab wann er E-Rechnungen ausstellen muss und was für den Empfang gilt – mit Datum und Paragraf (§ 27 Abs. 38 UStG, § 34a UStDV).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kundenYesAn wen wird fakturiert?
umsatzYesGesamtumsatz 2026 über oder bis 800.000 €?
betraegeYesLiegen alle Rechnungen unter 250 € (Kleinbetragsrechnungen)?
unternehmerYesIst die Person Unternehmer im Sinne des UStG?
kleinunternehmerYesGilt § 19 UStG (Kleinunternehmer)?

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, and the description's 'Beantwortet' is consistent with a read-only query. The description adds a useful scoping constraint ('für einen konkreten deutschen Betrieb') and indicates the output includes a date and legal paragraph, but it does not disclose how inputs are combined, edge cases, or response structure beyond that.

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?

A single, efficient sentence that front-loads the verb and subject, states the scope, and names the concrete deliverable (date and paragraph). There is no redundant or filler content.

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?

The description covers the core purpose and output shape, and the schema covers all inputs. It lacks explicit handling of edge cases or a detailed explanation of how parameters affect the result, but for a read-only advisory tool with self-describing parameters this is adequate.

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?

The schema has 100% coverage with descriptions for all 5 parameters, so the parameters are already well-documented. The tool description adds no parameter-specific detail, but given the high schema coverage, no further explanation is needed.

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 ('Beantwortet') and a specific resource ('einen konkreten deutschen Betrieb'), and states exactly what question it answers: when e-invoices must be issued and what applies for receiving them, with date and legal paragraph references. This clearly distinguishes it from siblings like pflichtangaben_pruefen (invoice content) or kleinunternehmer_pruefen (small business status).

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 description implies the usage context (a German business needing its e-invoice obligation determined) but never explicitly says when to use this tool instead of alternatives. No exclusions or alternative routing are mentioned, so an agent must infer selection from the title and description alone.

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

Most tools target distinct resources (customers, services, invoices, payments, legal checks), but rechnung_erstellen vs. leistungen_abrechnen both create invoices and rechnung_pruefen vs. pflichtangaben_pruefen both validate invoices, though with different inputs and scopes. The descriptions are detailed enough to resolve ambiguity after careful reading.

Naming Consistency3/5

The dominant pattern is object_verb (kunden_suchen, rechnung_erstellen, zahlung_erfassen), but several names are noun phrases (offene_posten, leistungen_offen, umsatzsteuer_aufstellung) or noun_noun (rechnung_datei). This mixed convention is still readable but lacks a uniform verb-based structure.

Tool Count3/5

At 18 tools, the server sits in the borderline-heavy range (16-25). The domain of German e-invoicing is broad, but the large number is not fully justified by tool specialization, as some functions (e.g., multiple validation tools) could be consolidated.

Completeness3/5

The surface covers the core lifecycle of services, invoicing, payment recording, and validation, but notable operations are missing: creating credit notes (explicitly referenced in zahlung_erfassen), managing customers, and updating/cancelling invoices. These gaps can lead to dead ends in common workflows like handling overpayments or customer changes.

Resources