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japan-law-util-mcp-server

japan-law-util-mcp-server

by gotoai

get_law_outline

Read-only

Retrieve a Japanese law's table of contents—parts, chapters, sections, articles, supplementary provisions, and appendices—with elm IDs for read_law_text, so you can read long laws in order.

Instructions

Get one law's table of contents: every part, chapter, section, article (with its caption), supplementary provision and appendix, in order, each with the elm value that reads it with read_law_text. Read this before reading a long law, and pass the elm it gives rather than building one yourself. Call read_law_text with the same include_amendment_supplements, which changes which supplementary provisions each elm names. Paged with offset.

Input Schema

NameRequiredDescriptionDefault
asofNoYYYY-MM-DD; the text in force then.
limitNo
offsetNo
law_id_or_numYesLaw id, law number or revision id.
include_amendment_supplementsNoList the amending laws' supplementary provisions too.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

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 and openWorldHint=true, so the safety profile is covered. The description adds real behavioral context beyond that: results are ordered, each entry carries an elm handle usable by read_law_text, paging is via offset, and the include_amendment_supplements flag must match on the follow-up read. It omits mention of result volume or the limit cap, which keeps it from a 5.

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?

Front-loaded with the return contents, then the workflow rule, then the coupling constraint. Sentences are dense but each carries distinct information. The trailing 'Paged with offset' is clipped and slightly out of place after the parameter-coupling guidance.

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?

An output schema exists, so return structure needn't be spelled out, and the description covers the essential agent-facing concerns: what the outline contains, why to call it first, how elm values link to read_law_text, the flag-consistency requirement, and offset paging. Minor gaps around asof and limit behavior are acceptable given the schema.

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 60%, so the description must add meaning, and it does for the two most consequential parameters: it explains that include_amendment_supplements changes which supplementary provisions each elm names (beyond the schema's terse 'List the amending laws' supplementary provisions too') and that paging is offset-based. asof and limit remain undocumented in the description, so it does not fully compensate for the coverage gap.

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?

States a specific verb and resource ('Get one law's table of contents') and enumerates exactly what is returned: parts, chapters, sections, articles with captions, supplementary provisions, appendices, in order, each with an elm value. This clearly distinguishes it from sibling read_law_text (which reads actual text) and the search tools.

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

Usage Guidelines5/5

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

Gives an explicit when-to-use rule ('Read this before reading a long law'), an explicit alternative-avoidance rule ('pass the elm it gives rather than building one yourself'), and a coordination constraint with a sibling ('Call read_law_text with the same include_amendment_supplements'). Nothing is left to inference.

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