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Konverter Pro

Answer account questions

answer_questions

Assign an account (4-8 digits, the user's chart) to whole question groups. For a booking type the answer is remembered for future files of the same client unless remember=false.

Input Schema

NameRequiredDescriptionDefault
answersYes
languageNoLanguage of messages (default de).
job_tokenYesThe job_token returned by upload_statements.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior4/5

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

Annotations declare a non-read-only, non-destructive, non-idempotent mutation, and the description adds real context beyond them: the answer is persisted for future files of the same client unless remember=false. That persistence side effect is exactly the kind of disclosure an agent needs and is not in the annotations. It does not cover failure modes or what happens to unmatched questions.

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?

Two tightly written sentences with the core action front-loaded and the persistence caveat second. No filler, though 'whole question groups' adds ambiguity rather than precision.

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?

No output schema exists, so the description should say more about outcomes — e.g. what is returned, whether partial answers are accepted, or what happens to an invalid account. For a mutation tool that also changes future state, the picture is adequate but incomplete.

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?

With 67% schema coverage, the description usefully explains the account format and the remember default. However, it never addresses the language parameter or the job_token linkage, and 'to whole question groups' is ambiguous against the schema's per-question_id array structure.

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 gives a concrete verb and resource: assigning an account number (4-8 digits, the user's chart) to question groups. That is a meaningful clarification of the otherwise vague name 'answer_questions'. It stops short of naming the sibling list_open_questions, so an agent must infer the pairing.

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 statement of when to call this versus list_open_questions, nor a prerequisite note that a job_token from upload_statements is required (only the schema says that). The one conditional it does give — remember=false — is about behavior, not tool selection.

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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