fynsight: MCP server for DATEV

answer-clarification

Answer an open clarification question raised for an anomaly case. Records the answer only; it does NOT approve any correction (corrections are approved by the user in the app).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "clarification_id": "<clarification_id>",
      "answer": "<answer>"
    },
    "name": "answer-clarification"
  }
}

Input Schema

Name	Required	Description
`answer`	Yes	The answer (1–4000 chars)
`suggestion_id`	No	Only if the user picked one of the suggestions of the question (clarification.suggestions[].id); the answer text is still required and stored as sent
`clarification_id`	Yes	UUID of the clarification question

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 7, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already mark this as a non-read-only, non-destructive write, and the description usefully adds what the write does and does not do: it records the answer only and explicitly does not approve a correction, which happens in the app. It does not disclose what happens on repeat calls (idempotentHint=false) or when the question is already closed, leaving some behavioral gaps.

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?

Two short sentences, front-loaded with the action and immediately followed by the most important scoping caveat. No filler or redundant restatement of the title.

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?

For a three-parameter write tool with no output schema, the description covers purpose and the approval boundary adequately. It omits behavior for edge cases an agent would care about: duplicate answers given idempotentHint=false, error/response behavior, and any indication of whether the question must still be open.

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% and all three parameters carry their own descriptions, including the conditional nature of suggestion_id, so the schema does the heavy lifting. The description adds no additional meaning about the parameters beyond restating the answer/approval distinction.

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?

States a specific verb and resource: 'Answer an open clarification question raised for an anomaly case.' It also draws a sharp boundary against the correction workflow ('it does NOT approve any correction'), which separates it from sibling tools like propose-transaction-correction. It stops short of naming a sibling it could be confused with, such as note-anomaly-case.

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?

Usage is implied by the precondition 'open clarification question' and the negative guidance that this is not an approval step. However, no explicit when-to-use/when-not-versus-alternatives routing is given (e.g., note-anomaly-case, propose-manual-adjustment), so the agent must infer the selection rule.

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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