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LimEulYoung

legal-search-mcp

by LimEulYoung

compute_sentencing_range

Read-onlyIdempotent

Determine Korean criminal sentencing ranges by entering the charge and optional statutory, guideline, and probation factors, then validate a proposed sentence against official guidelines.

Instructions

통합 양형 도구 — 죄명에서 법정형→처단형→권고형→선고 검증까지, 인자를 채울수록 깊은 단계로 자동 진행: charge만=lookup(법정형·권고범위·인자 enum) / +statutory_modifications=처단형(형법§56 순서 적용) / +guideline_type·guideline_factors=권고형 / +sentence_months·fine_amount(+probation_factors)=final(선고형·집행유예 검증). 결과는 양형기준이 정한 '범위'(예측 아님). 호출 간 상태가 없으므로 후속 호출마다 charge와 확정 선택·플래그·offense_date를 반복하고 새 인자를 추가. Args: charge=판결문형 죄명 문자열(예 '살인','도로교통법위반(음주운전)') — 호출당 하나(여러 죄는 각각 호출; list면 죄명별 유도[multiple_charges]), 숫자·ID 불가(조문번호·charge_id 아님; 숫자면 유도[charge_numeric]). offense_date=행위 일자(지정 시 행위시 조문). sg_category_id·statute_choice·branch_key·reference_choice=ambiguous_* 응답이 후보를 줄 때. is_attempted·is_accessory·is_solicitor=미수·방조·교사. statutory_modifications=가중감경 list(lookup enum에서). guideline_type·guideline_leaf_id·guideline_factors=권고유형(lookup 목록 명칭 그대로)·leaf_id·특별인자. sentence_months=검증 선고형(자유형·월)·fine_amount=벌금(원)·probation_factors=집행유예 인자(dict). act_count=동종 다행위 수(≥2면 경합범 가중 자동). 후속 단계 값은 이전 응답 enum에 있는 key만 쓰고 추측 금지. 응답의 '출처'(해설서 PDF)가 있으면 인용 링크로 제시.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chargeYes
act_countNo
branch_keyNo
fine_amountNo
is_accessoryNo
is_attemptedNo
is_solicitorNo
offense_dateNo
guideline_typeNo
sg_category_idNo
statute_choiceNo
sentence_monthsNo
reference_choiceNo
guideline_factorsNo
guideline_leaf_idNo
probation_factorsNo
statutory_modificationsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description discloses stateless multi-step behavior, range vs. prediction semantics, the need to reuse enums from prior responses, and automatic treatment of multi-act counts. This gives the agent a realistic model of how the tool behaves across calls.

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?

The description is dense but highly structured, front-loading the tool's purpose and stage progression before the argument details. Every sentence adds operational value, and the length is justified by the number of parameters and the multi-stage workflow.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, 17 parameters, and existing output schema, the description is remarkably complete: it covers all parameter semantics, call sequencing, statelessness, enum reuse, and citation handling. An agent has enough information to invoke the tool correctly across successive calls.

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

Parameters5/5

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

Schema description coverage is 0%, so the description carries the full burden of explaining 17 parameters. It does so thoroughly: charge format constraints, offense_date semantics, ambiguous_* response parameters, statutory_modifications, guideline fields, sentence/fine/probation inputs, and act_count behavior are all explained with concrete examples and constraints.

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 clearly identifies the tool as a unified sentencing computation engine that progresses from statutory range to final sentence verification, with a specific verb and resource. It does not explicitly name sibling tools for differentiation, but the scope is unmistakable.

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 provides strong contextual guidance: it explains the staged progression based on which parameters are filled, notes statelessness, and tells the caller to repeat charge and confirmed selections on each call. It does not explicitly state when to prefer this tool over a sibling like precedent_search or sentence_statistics, but 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.