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UpstageAI

korean-tax-calc-mcp

unfair_transaction

Read-onlyIdempotent

Check if related-party transactions trigger denial under Korean corporate tax rules by comparing market and actual unit prices; flags a ₩300M or 5%+ difference.

Instructions

부당행위계산 기준(시가와 실제 거래가 차이 3억 원 이상 또는 5% 이상). 언제: 특수관계인 거래 prices가 시가와 크게 차이 나 부당행위계산 부인 대상인지 확인할 때. 입력: 시가(단가, 원) → market_price, 실제 거래 단가(원) → actual_price, 수량 → quantity. 결과: 시가총액·차액·차액비율·부당행위계산 여부, 근거(법인세법 제52조, 시행령 제88조③). 부당행위계산 기준(시가와 실제 거래가 차액이 3억 원 이상 또는 5% 이상이면 부인 대상).

Input Schema

NameRequiredDescriptionDefault
quantityYes수량
actual_priceYes실제 거래 단가(원)
market_priceYes시가(단가, 원)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.9.1

TDQS

A3.8/5.0
Behavior4/5

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

The annotations already establish that this is a read-only, idempotent, non-destructive calculation. The description adds useful behavioral context by naming the returned result fields (market total amount, difference, difference ratio, whether denial applies) and the legal basis, which helps the agent understand what the tool produces.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description uses helpful labels (언제, 입력, 결과) and is front-loaded, but it repeats the unfair act calculation threshold at both the beginning and the end. The duplication costs efficiency, keeping it at an adequate rather than excellent level.

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?

There is no output schema, but the description compensates by listing the expected result fields and citing the legal basis. Together with the 100% schema coverage and clear usage condition, an agent has enough context to call the tool correctly, though additional edge-case guidance could improve it.

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 the schema already documents each parameter with Korean labels. The description maps the same inputs (market_price, actual_price, quantity) and units, adding little beyond what the schema provides. A baseline 3 is appropriate when the schema does the heavy lifting.

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 identifies a specific tax calculation: checking whether a related-party transaction falls under the unfair act calculation denial standard. It gives the legal concept and threshold, making the purpose clear without opening the schema. It does not explicitly contrast with any sibling tool, so it stops short of a 5.

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?

It explicitly states when to use the tool: when a related-party transaction price differs significantly from market price and you need to check whether it is subject to unfair act calculation denial. No alternatives or when-not-to-use guidance are given, so it is clear but not exhaustive.

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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