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article_amendment_diff

Read-onlyIdempotent

Compare a specific article's text across two Korean law versions using MST values, showing side-by-side line-level differences to identify amendments.

Instructions

Compare how a specific article changed between two law versions (조문 신구대조).

Use MST values from law_amendment_summary to compare article text across revisions.

Args: mst_old: MST of the older version (법령일련번호, from law_amendment_summary) mst_new: MST of the newer version article: Article number (e.g., 52 for 제52조) article_branch: Branch number (e.g., 2 for 제52조의2, default 0) oc: Optional OC override type: Response format - "JSON" (default), "XML", or "HTML"

Returns: Side-by-side comparison with line-level diff

Examples: >>> article_amendment_diff(mst_old="269000", mst_new="273000", article=52)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ocNo
typeNoJSON
articleYes
mst_newYes
mst_oldYes
article_branchNo
Behavior4/5

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds valuable context by specifying what is compared (article text across revisions) and the output format (side-by-side line-level diff), which goes beyond annotation information. 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 concise and well-structured: a one-sentence purpose, a usage tip, a clear Args list, a Returns section, and an example. Every line adds value; there is no redundancy or fluff.

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 6-parameter tool with no output schema, the description is complete: it documents all parameters, return format, provides a concrete example, and explains the prerequisite relationship with law_amendment_summary. This is sufficient for an agent to invoke the tool correctly without additional information.

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 must fully document parameters. It explains all 6 parameters: mst_old (older version, from law_amendment_summary), mst_new (newer version), article (number with example), article_branch (branch number with example and default), oc (optional override), and type (format with default and options). The example call further clarifies usage.

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 a specific action ('Compare how a specific article changed between two law versions') with a resource (specific article, two MST versions). It distinguishes from siblings by referencing MST values from law_amendment_summary, making its role in the workflow explicit.

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 gives clear context: 'Use MST values from law_amendment_summary to compare article text across revisions.' This implies a prerequisite workflow and tells users where to obtain the required MST parameters. It does not explicitly list alternative tools or when not to use, but the context is strong enough for correct usage.

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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