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narumiruna

Taiwan Legislative Yuan MCP Server

list_laws

Retrieve and filter Taiwan's legislative laws by number, category, status, authority, or date to access structured legal information from the Legislative Yuan database.

Instructions

列出立法院法律列表。

Args:
    law_number: 法律編號,例:09200015
    category: 類別,母法或子法
    parent_law_number: 母法編號,例:09200
    law_status: 法律狀態,例:現行
    authority: 主管機關,例:總統府
    latest_version_date: 最新版本日期,格式:YYYY-MM-DD,例:2024-10-25
    page: 頁數,預設1
    limit: 每頁筆數,預設20,建議不超過100
    output_fields: 自訂回傳欄位(如需指定欄位,請填寫欄位名稱列表)

Returns:
    str: JSON 格式的法律查詢結果。

Raises:
    例外時回傳中文錯誤訊息字串。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
law_numberNo法律編號,例:09200015
categoryNo類別,母法或子法
parent_law_numberNo母法編號,例:09200
law_statusNo法律狀態,例:現行
authorityNo主管機關,例:總統府
latest_version_dateNo最新版本日期,格式:YYYY-MM-DD,例:2024-10-25
pageNo頁數,預設1
limitNo每頁筆數,預設20,建議不超過100
output_fieldsNo自訂回傳欄位(如需指定欄位,請填寫欄位名稱列表)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that returns are in JSON format and errors in Chinese messages, which is useful. However, it doesn't mention pagination behavior beyond parameter defaults, rate limits, authentication needs, or what happens with null parameters (treated as optional filters). Adds some context but gaps remain.

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 clear sections (Args, Returns, Raises) and front-loaded purpose. However, the Args section repeats schema info unnecessarily, and the description could be more concise by omitting redundant parameter details. Efficient but with some waste.

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 9 parameters, no annotations, and an output schema (implied by Returns), the description is reasonably complete. It covers purpose, parameters, return format, and error handling. However, it lacks behavioral details like pagination mechanics or sibling tool differentiation, leaving minor gaps for a list/filter tool.

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 9 parameters thoroughly. The description repeats parameter info in the Args section but adds minimal extra meaning (e.g., '建議不超過100' for limit). Baseline is 3 as schema does heavy lifting, with slight value from usage hints.

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

Purpose3/5

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

The description states '列出立法院法律列表' (List Legislative Yuan law list), which provides a basic verb+resource but lacks specificity about filtering capabilities. It doesn't distinguish from siblings like 'get_law' (retrieve specific law) or 'list_law_contents' (list law contents). The purpose is clear but generic.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like 'get_law' (for specific law details) or 'list_law_contents'. The description implies filtering via parameters but doesn't explicitly state when to use this tool over others, leaving the agent to infer usage context.

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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