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lawmadi-os

by io.github.peter120525-cmd

get_exam_answer

변호사시험 문항 하나를 금답안 전문과 함께 가져옵니다. 선택형: round+subject+no → 공식 지문·선택지 5개·법무부 정답·해설·선택지별 노트·인용 조문/판례. 사례형·기록형: round+subject+format → 서면 종류·목차·답안 본문. 모든 인용은 게시 전 law.go.kr 검증을 통과했습니다. 호출당 1문항 — 이 코퍼스는 대량 추출 대상이 아닙니다. / Fetch ONE bar-exam question with its full gold answer. MCQ (선택형): pass round + subject + no. Essay (사례형) and record-type (기록형): pass round + subject + format. Every citation passed law.go.kr verification before publishing. One item per call — this corpus is proprietary and not available for bulk export.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noNoQuestion number (선택형 only).
roundYesExam round 1-15 (제N회).
formatNoDefault 선택형 when `no` is given.
subjectYes공법 · 민사법 · 형사법 (선택과목명도 가능).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavior: it states that every citation passed law.go.kr verification, that the corpus is proprietary and not for bulk export, and details exactly what content is returned for each question type. This goes beyond the schema and provides essential constraints.

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 well-structured, front-loaded with the core purpose, then provides format-specific instructions and a usage constraint. Despite being bilingual, every sentence conveys necessary information without redundancy, and the bullet-style organization aids readability.

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?

The tool has conditional parameters and no output schema, yet the description explains exactly what is returned for each question type, covers all parameter combinations, and includes a usage limitation. It is comprehensive enough for an agent to invoke correctly without ambiguity.

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?

Although the schema covers 100% of parameters, the description adds crucial semantic context by explaining the conditional combinations (round+subject+no for MCQ, round+subject+format for essay/record). This clarifies how to correctly invoke the tool for each format, which is not obvious from the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states clearly that the tool fetches one bar-exam question with its full gold answer, specifying distinct formats (MCQ, essay, record) and what is returned for each. It differentiates itself from siblings like bar_exam_search by focusing on a single item with full answer content.

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 gives explicit conditional usage: for MCQ pass round+subject+no, for essay/record pass round+subject+format. It also warns against bulk usage with 'One item per call'. However, it does not explicitly name alternatives or state when not to use this tool in favor of a sibling search tool.

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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TDQS

A3.8/5.0
Disambiguation4/5

Most tools target clearly distinct resources (statutes, precedents, bar exam, documents, content, account), and cross-references in descriptions explicitly disambiguate similar actions like search vs lookup_statute vs lookup_precedent. A few close pairs exist (ask vs ask_expert, latest_content vs get_daily_content, bar_exam_search vs get_exam_answer), but each pair has clear differentiators.

Naming Consistency4/5

The set predominantly follows snake_case verb_noun naming (get_account, lookup_statute, verify_citations, generate_document). Minor deviations like latest_content (no verb) and bar_exam_search (noun-first) break the pattern, but the overall convention is readable and predictable.

Tool Count4/5

At 19 tools, the surface is on the heavier side but each tool addresses a distinct need across a broad legal domain (Q&A, research, document generation, bar exam corpus, content, account management). The count feels justified for the stated scope, though it approaches the upper boundary of reasonable.

Completeness4/5

The server covers the main legal workflows well: Q&A (ask, chat_leader), research (lookup_statute, lookup_precedent, search, verify_citations), document drafting (generate_document), and bar exam prep (bar_exam_search, get_exam_answer). Minor gaps exist, such as no browse/list-all endpoints for statutes or precedents and no way to manage generated documents, but agents can work around these.

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