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mf-api-mcp

mfc_ca_postTransactionJournalize

Creates a journal entry from a transaction detail, requiring only the counterpart account ID and auto-completing the rest. User approval is required.

Instructions

明細から仕訳を作成します(帳簿書き込み。要ユーザー承認)。相手科目account_idのみ必須。貸借方向・口座側科目・税区分・invoice_kindはMFが自動補完。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
memoNo
tagsNo
remarkNo
tax_idNo
account_idYes相手勘定科目ID
office_codeNo対象事業者の事業者番号(XXXX-XXXX)。APIキー認証では必須。MF_OFFICE_CODE を設定していれば省略可
invoice_kindNo公式書込み3値以外も送信可(検証実験用)
department_idNo
sub_account_idNo
transaction_idYes明細ID(URLエンコード済みのまま)
transaction_dateNo省略時は明細の取引日
trade_partner_codeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and discloses genuine behavioral traits: it performs a book-writing mutation (帳簿書き込み), requires user approval (要ユーザー承認), and MF auto-completes debit/credit direction, account-side subject, tax classification, and invoice_kind. This goes well beyond a bare 'creates journals' claim.

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?

Three compact Japanese sentences with zero filler. The purpose is front-loaded, and every clause earns its place: the action, the approval constraint, the required parameter, and the auto-completion behavior.

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 12-parameter mutation tool with no annotations and no output schema, the description covers the essential call-time facts competently. It does not describe the response shape, the approval flow (e.g., pending status or deferred execution), or error/failure conditions, which an agent needs for a complete mental model of this multi-step operation.

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 only 42%, so the description must compensate. It usefully clarifies that transaction_id and account_id are the required inputs and that tax_id, invoice_kind, and other fields are auto-completed by MF. However, it leaves memo, tags, remark, department_id, sub_account_id, and trade_partner_code unexplained, and the auto-completion statement slightly tensions with invoice_kind's schema note allowing experimental values.

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-chain and resource: creating journal entries (仕訳) from transaction details (明細), which distinguishes it from siblings like postJournals and postTransactions. The source-scope ('from transactions') and the auto-completion note differentiate it behaviorally, though it never names a sibling explicitly.

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?

Implied usage context is clear: call this when you have a transaction_id and want to generate journals from it, and only account_id must be supplied. However, it gives no explicit when-not-to-use guidance or comparison to the closely related mfc_ca_postJournals sibling, leaving tool routing to inference.

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