Skip to main content
Glama
lawchat-oss

mcp-taiwan-legal-db

by lawchat-oss

get_pcode

Convert Taiwanese law names to pcode codes for the National Legal Database. Supports over 11,700 regulations with fuzzy matching.

Instructions

將法規名稱轉換為全國法規資料庫的 pcode 代碼。

涵蓋 11,700+ 部法規(法律 + 命令),支援模糊比對。

Args: law_name: 法規名稱(如「民法」「勞基法」「消保法」)

Returns: 包含 pcode 的字典,或模糊比對建議

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
law_nameYes
Behavior4/5

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

No annotations are provided, but the description discloses the tool's behavior: it performs a lookup with fuzzy matching, returns a dictionary with pcode or suggestions. It does not mention rate limits or auth, but for a read-only lookup this is adequate.

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 succinct with no wasted words. It front-loads the purpose, then provides coverage, parameter details, and return format in a clear, structured way.

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 the tool's simplicity (1 parameter, no output schema), the description covers the essential: purpose, coverage, fuzzy matching, and return type. It could mention error cases or multiple matches, but overall it is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description compensates by explaining the 'law_name' parameter with examples like '民法' and '勞基法'. It clarifies that the input is a law name and implies that fuzzy matching is supported, adding value beyond the schema's bare 'Law Name' title.

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 uses a specific verb '轉換' (convert) and clearly states the resource: law names to pcode. It covers 11,700+ regulations, supports fuzzy matching, and is distinct from siblings like get_citations or get_judgment which serve different purposes.

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 implies usage: when you have a law name and need its pcode. It provides context (coverage, fuzzy matching) but lacks explicit when-not-to-use or alternative tool comparisons. The sibling list provides indirect guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/lawchat-oss/mcp-taiwan-legal-db'

If you have feedback or need assistance with the MCP directory API, please join our Discord server