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Validate a DATEV EXTF file

validate_datev_extf
Read-only

Validate DATEV EXTF CSV files before import or after rejection. Get line, field, and plain-language explanations for issues like decimal commas, leading zeros, account length, encoding, and header errors.

Instructions

Check a DATEV-Format (EXTF) CSV file, e.g. EXTF_Buchungsstapel.csv or a Debitoren/Kreditoren export, before importing it into DATEV or after DATEV rejected it. Returns every problem with line, field and a plain-language explanation in German or English: dot instead of comma in amounts, missing leading zeros in Belegdatum, BU-Schlüssel on automatic accounts, account length, encoding, header errors and more. Pass a file path (preferred, the encoding is checked too) or the file content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage of the messages: de (default) or en.de
pathNoPath to the CSV file on this machine. Relative paths resolve against the server's working directory.
contentNoThe file content as text, if there's no file. The encoding can't be checked this way.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
errorsYes
headerNo
categoryNobuchungsstapel, debkred or other
encodingYes
findingsYes
warningsYes
truncatedYes
disclaimerYes
countsByCodeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, setting the safety profile. The description adds useful behavior beyond that: it discloses that passing a file path enables encoding checks, while passing content does not, and it describes the output format (line, field, plain-language explanation). This adds context without contradicting 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?

The description is informative but not bloated. It front-loads the purpose, lists concrete example problems, and explains the two input modes in a single paragraph. Every sentence earns its place, though the list of error types could be trimmed without losing meaning.

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?

For a validation tool with an output schema, the description covers the essential usage details: what it validates, when to use it, input modes, language selection, and the general shape of results. It doesn't mention all edge cases or limitations, but the output schema provides the return structure, and the annotation readOnlyHint covers side effects. Missing any explicit handling of missing required fields (none are required) is minor.

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

Parameters4/5

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

Schema description coverage is 100% and each parameter has a basic description. The tool description adds value by clarifying that 'path' is preferred because it enables encoding checks, and that 'content' cannot have encoding checked. This is extra semantic meaning not present in the schema properties themselves.

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 states a specific action ('Check a DATEV-Format (EXTF) CSV file') and the resource (DATEV EXTF files like EXTF_Buchungsstapel.csv). It clearly distinguishes from siblings by specifying it returns problems with line, field, and plain-language explanations, which is distinct from listing columns, explaining a finding, or writing a file.

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?

The description gives explicit when-to-use scenarios: 'before importing it into DATEV or after DATEV rejected it.' It also advises passing a file path as preferred over content. It doesn't name alternatives or state when not to use it, but the sibling tools are dissimilar enough that the usage context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.