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kannkyo

e-gov-law-mcp

by kannkyo

法令改正履歴取得

get_law_revisions

Retrieve revision history for a specified Japanese law, with optional filters for amendment type and effective date range.

Instructions

指定した法令の改正履歴一覧を取得する(e-Gov法令API v2 GET /law_revisions/{law_id_or_num})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
law_id_or_numYes法令ID または 法令番号
amendment_typeNo改正種別(1:新規, 3:被改正, 8:廃止。複数指定可)
amendment_date_toNo施行期日の範囲終了(YYYY-MM-DD)
amendment_date_fromNo施行期日の範囲開始(YYYY-MM-DD)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral transparency. It only mentions the API endpoint and does not disclose whether the operation is read-only, what the response format is, whether pagination or rate limits exist, or any error conditions. This is especially lacking for a retrieval tool with potentially complex output.

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 a single sentence that is specific and front-loaded, with the API endpoint added in parentheses for extra context. There is no redundancy or wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimal and does not explain the return value or provide any additional context about the amendment history response. However, the schema covers parameters, and the purpose is clear, so it is minimally viable for a simple retrieval operation.

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?

The input schema already describes all four parameters with 100% coverage, so the description adds no additional parameter semantics. The baseline of 3 is appropriate since the schema does the heavy lifting, and the description does not compensate with extra context.

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 the action ('取得する' - retrieve) and the specific resource ('改正履歴一覧' - amendment history list) and even references the exact API endpoint. This unambiguously distinguishes it from sibling tools like search_laws, get_law_data, or download_law_file.

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 is provided on when to use this tool versus alternatives. The description does not mention any exclusions, prerequisites, or comparisons with sibling tools, leaving the agent to infer usage solely from the tool name and description.

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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