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lawchat-oss

mcp-taiwan-legal-db

by lawchat-oss

search_judgments

Search Taiwan court judgments by keyword, court, case type, year, case number, or main text. Filter by winner/loser using structured main text keywords.

Instructions

搜尋司法院裁判書系統。

結果自動按法院權威性排序(最高法院→高等法院→地方法院),同層級按原始排序。 每筆結果含 court(法院名稱)、case_type(民事/刑事/行政)、court_level(1=最高/2=高等/3=地方)。

【重要】查特定案號時,必須用 case_word + case_number(精確查詢),不要把案號放在 keyword。 例如查「114年度上易字第503號」→ case_word="上易", case_number="503", year_from=114。 keyword 僅用於主題式全文檢索(如「預售屋 遲延交屋」)。

【進階實務研究欄位】:

  • main_text: 裁判主文關鍵字 — 最有效的輸贏方篩選方式。 主文措辭高度制度化(依民刑訴訟法條生成),substring match 接近 解析半結構化欄位,精度高:

    • 「被告應將 移轉」→ 被告敗訴(物權移轉類)

    • 「被告應給付」→ 被告敗訴(金錢給付類)

    • 「原告之訴駁回」→ 原告敗訴

    • 「上訴駁回」→ 維持原審 可與 keyword 併用,例: 找「借名登記成立、被告敗訴」→ main_text="被告應將 移轉", keyword="借名登記", case_type="民事"

Args: keyword: 全文檢索關鍵字(對應 jud_kw) court: 法院名稱(如「最高法院」「臺灣高等法院」「臺灣臺北地方法院」) case_type: 案件類型(民事/刑事/行政/懲戒) year_from: 起始年度(民國年,如 110) year_to: 截止年度(民國年,如 113) case_word: 字別(如「台上」「上易」「重訴」),查特定案號時必填 case_number: 案號(數字),查特定案號時必填 main_text: 裁判主文關鍵字(對應 jud_jmain)— 結構化篩選輸贏方 max_results: 回傳筆數上限(預設 10,上限 200)

Returns: 包含搜尋結果的字典:success, query, total_count, results, cached, timestamp

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordNo
courtNo
case_typeNo
year_fromNo
year_toNo
case_wordNo
case_numberNo
main_textNo
max_resultsNo
Behavior4/5

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

No annotations provided; description bears full burden. Discloses sorting by court hierarchy, returned fields (court, case_type, court_level), and that main_text is semi-structured. Does not cover auth needs or rate limits.

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?

Well-structured with sections and examples. Somewhat lengthy due to detailed main_text variants, but all information is relevant and adds value. Front-loaded with purpose and key usage.

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?

Comprehensive for a complex tool with 9 parameters. Covers sorting, caching, return format, parameter interactions, and advanced filtering. No output schema, but description details the return structure.

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 has no parameter descriptions (0% coverage). Description adds full semantics: keyword for full-text, court name, case_type domain, ROC year format, case_word/number for exact lookup, main_text for win/loss, max_results default and limit.

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?

Description starts with '搜尋司法院裁判書系統' (Search the Judicial Yuan judgment system), clearly stating the verb and resource. It is distinct from sibling tools like get_judgment (single judgment fetch) and search_interpretations.

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?

Provides explicit guidance on when to use keyword vs case_word/case_number, with a concrete example. Describes advanced main_text usage for win/loss filtering. Does not explicitly mention when not to use or compare with sibling tools.

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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