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oldbear-meme

mcp-taiwan-legal-db-integrated

by oldbear-meme

query_regulation

Query Taiwan's national regulations database for specific articles, article ranges, or full text, with optional legislative history.

Instructions

查詢全國法規資料庫的法規條文。

可查詢單一條文、條號範圍、或法規全文。

Args: law_name: 法規名稱(如「民法」「勞動基準法」),會自動轉換為 pcode pcode: 法規代碼(如「B0000001」),若提供 law_name 可不填 article_no: 條號(如「184」「247-1」「15-1」),查詢單一條文 from_no: 起始條號(如「184」),查詢條號範圍時使用 to_no: 截止條號(如「198」),查詢條號範圍時使用 include_history: 是否包含修法沿革(使用者詢問修法歷程、修正時間、歷次修正內容時設為 True)

Returns: 包含法規條文的字典:law (pcode, name, status), articles, source_url, history(選填)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
law_nameNo
pcodeNo
article_noNo
from_noNo
to_noNo
include_historyNo
Behavior4/5

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

No annotations are provided, so the description fully addresses behavioral aspects. It discloses automatic law_name-to-pcode conversion, the return structure (law, articles, source_url, optional history), and the functional scope. No destructive behavior is expected, and the transparency is sufficient.

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 well-structured with a clear purpose statement and bullet-style parameter explanations. It is slightly verbose but every part contributes to understanding. Concise enough for an AI agent to parse efficiently.

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?

Given 6 parameters, 0% schema coverage, and no output schema, the description covers all parameters and return structure. It could mention error handling or prerequisites (e.g., either law_name or pcode required), but it is largely complete for the tool's complexity.

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 compensates fully. It explains each parameter's purpose (e.g., law_name vs pcode, article_no for single article, from_no/to_no for range, include_history for history) with examples like '民法' and '247-1'. This adds significant meaning beyond the raw 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 clearly states it queries the national regulations database, specifying the types of queries (single article, range, full text). This distinguishes it from siblings like search_regulations, which likely searches across regulations rather than retrieving specific articles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains parameter usage (e.g., include_history for history queries) but does not explicitly guide when to use this tool versus alternatives like search_regulations or get_citations. Usage context is implied but lacks exclusions or alternative recommendations.

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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