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lawmadi-os

by io.github.peter120525-cmd
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generate_document

[KEY REQUIRED · no credits, 20/day] 사용자의 사실관계로 한국 법률 문서 초안을 작성합니다(고소장·소장·내용증명·답변서·합의서·법률의견서 등). 초안의 모든 조문·판례 인용은 ask와 동일한 fail-closed law.go.kr 검증을 거칩니다. 유효한 doc_type 키는 list_document_types로 먼저 확인하세요. / Draft a Korean legal document from the user's facts. Every statute/precedent citation in the draft goes through the same fail-closed law.go.kr verification as ask. Use list_document_types first for valid doc_type keys.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoDefault ko.
contextYesThe user's facts/situation the document must be built from.
doc_typeYesDocument type key from list_document_types (e.g. 'complaint').
client_tagNo선택. client_tag: 호출자 식별용, **응답에 영향 없음**(8~64자, 영숫자·_-.). 서버 로그에만 남아 호출을 골라 세는 데 씁니다. 형식이 어긋나면 무시합니다 — 거절하지 않습니다. 질의 본문에 섞지 마세요: 본문에 넣으면 질문의 일부가 되어 분류와 검증 결과가 달라집니다. / Optional caller label (8-64 chars). Logged only; never affects the response. Invalid values are ignored, not rejected. Do not put it in the query text — there it becomes part of the question.
extra_instructionsNoOptional extra drafting instructions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / client_tag
      Added value: +{
+  "description": "선택. client_tag: 호출자 식별용, **응답에 영향 없음**(8~64자, 영숫자·_-.). 서버 로그에만 남아 호출을 골라 세는 데 씁니다. 형식이 어긋나면 무시합니다 — 거절하지 않습니다. 질의 본문에 섞지 마세요: 본문에 넣으면 질문의 일부가 되어 분류와 검증 결과가 달라집니다. / Optional caller label (8-64 chars). Logged only; never affects the response. Invalid values are ignored, not rejected. Do not put it in the query text — there it becomes part of the question.",
+  "type": "string"
+}
  2. Added

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses fail-closed law.go.kr verification for every citation, quota/credit limits, and key requirement. It does not explain failure modes or output format, but it provides meaningful behavioral context beyond a bare 'generates a document' statement.

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

Conciseness4/5

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

The description is compact and front-loaded with the key constraint, then states purpose, verification behavior, and prerequisite. The bilingual repetition adds length but serves the audience; every substantive point earns its place.

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?

The tool is moderately complex and has no output schema, yet the description covers validation prerequisites, citation verification, document variety, and quota limits. It does not state the return shape or error behavior, but the essential call details are present.

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%, so the schema already documents all five parameters. The description reinforces that doc_type must come from list_document_types and explains verification behavior, but it does not add much new parameter-level meaning beyond the schema.

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 names a specific verb and resource: 'Draft a Korean legal document from the user's facts' and enumerates concrete document types (고소장·소장·내용증명·답변서·합의서·법률의견서). It also points to list_document_types for valid keys, distinguishing it from legal research or Q&A siblings.

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 clearly states a precondition: use list_document_types first to obtain a valid doc_type. It also communicates operational constraints (key required, no credits, 20/day limit). It does not explicitly contrast with alternatives like ask, but the document-drafting context makes the intended use apparent.

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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