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LexLink-ko-mcp

LexLink-ko-mcp

by joohyukjung
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

law_amendment_summary

Read-onlyIdempotent

Retrieves revision history of Korean laws within a date range, showing when and how each amendment occurred. Use returned MST IDs to compare specific article changes.

Instructions

List all revisions of a law within a date range (법령 개정 이력 조회).

Shows when and how a law was amended over time. Use the MST values from the results with article_amendment_diff to see specific article changes.

Args: law_name: Law name (e.g., "근로기준법") date_from: Start date YYYYMMDD (default "20100101") date_to: End date YYYYMMDD (default "20261231") oc: Optional OC override type: Response format - "JSON" (default), "XML", or "HTML"

Returns: List of revisions with dates, types, and MST identifiers

Examples: >>> law_amendment_summary(law_name="근로기준법", date_from="20200101") >>> law_amendment_summary(law_name="민법", date_from="20150101", date_to="20251231")

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "law_name": "<law_name>"
    },
    "name": "law_amendment_summary"
  }
}

Input Schema

NameRequiredDescriptionDefault
ocNo
typeNoJSON
date_toNo20261231
law_nameYes
date_fromNo20100101

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: the tool lists revisions across a date range, reveals the default date window (20100101 to 20261231), and indicates that results include MST identifiers for use with article_amendment_diff. No contradictions with annotations.

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 well-structured with a concise one-sentence summary, an Args section, a Returns section, and two usage examples. Every sentence earns its place; no fluff or redundant repetition of the schema. It is front-loaded with the main purpose, making it easy for an agent to scan.

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?

For a tool with 5 parameters, no output schema, and moderate complexity, the description is highly complete. It explains the return value (list of revisions with dates, types, MST identifiers), provides default date ranges, and links to a sibling tool for deeper analysis. The examples illustrate typical usage. This is on par with the 'get_calls' calibration example and arguably richer.

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?

Schema coverage is 0%, so the description must carry the full burden. It provides clear formats and defaults for law_name, date_from, date_to, and type, with examples for law_name. The 'oc' parameter is only labeled 'Optional OC override,' which is vague, but the description still offers more than the schema (which has no descriptions). Given the near-complete compensation, a score of 4 is appropriate.

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 opens with a specific verb+resource+scope: 'List all revisions of a law within a date range.' It further clarifies what the tool shows ('when and how a law was amended over time') and distinguishes it from sibling tools like article_amendment_diff by referencing MST values for follow-up. This makes the purpose unmistakable and distinct.

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 provides clear context: use this tool to query amendment history within a date range. It explicitly points to article_amendment_diff as the next step for seeing specific article changes, which is an alternative/follow-up tool. However, it does not explicitly state exclusions or when not to use it, so it falls just short of a perfect score.

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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