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uedayou

loa-mcp-server

by uedayou

町丁目配下の番地一覧

list_banchi

Enumerate all banchi (land numbers) belonging to a specified Japanese town or chome. Automatically resolves omitted administrative names and variant kanji for accurate results.

Instructions

指定した町丁目に属する番地を列挙する。丁目のある町は丁目ごとの番地、丁目のない町はそのまま番地一覧になる。号(建物番号)は元データに含まれないため取得できない。「〇〇郡△△町」の郡名、政令指定都市の市名の省略、「ケ/ヶ/ヵ」等の異体字表記ゆれは自動補完を試みる。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
townYes町丁目レベルの住所文字列またはURI(丁目番号を含まない。例: '東京都新宿区歌舞伎町')。「〇〇郡△△町」の郡名や、政令指定都市の市名の省略(例: '東京都瑞穂町'、'神奈川県南区')、「ケ/ヶ/ヵ」等の異体字表記ゆれは自動的に補完を試みる。
chomeNo丁目番号で絞り込む(例: 2)。省略した場合、丁目ありの町については全丁目分の番地をまとめて返す。
limitNo
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses auto-completion of input variations (gun names, city omissions, variant characters), output scope (banchi only, not building numbers), and chome aggregation behavior. These are meaningful behavioral insights beyond the schema, though it does not mention error handling or pagination.

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 compact three-sentence paragraph, each sentence adding distinct information: core function, chome behavior, and input normalization. No filler or redundancy; it is efficiently structured and front-loaded.

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?

For a tool with no output schema, the description sufficiently explains inputs and behavioral nuances. It covers town normalization, chome handling, and limitations, making it self-contained for selecting and invoking the tool. Minor gaps like return format and limit semantics prevent a perfect score.

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 67%, so the description adds valuable context for the 'town' parameter by explaining auto-completion, and for 'chome' by clarifying that omission returns all chome results aggregated. The 'limit' parameter is not described, but other parameters receive depth beyond their schema descriptions.

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 tool enumerates banchi (lot numbers) for a specified town, matching the title. It differentiates from siblings by specifying the resource type (banchi under a town) and notes chome handling, which is distinct from generic address search or child address listing.

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 on when to use the tool (to list banchi for a town) and explains behavior for towns with and without chome. It also states a limitation (building numbers are not obtainable), but doesn't explicitly name alternative tools or provide exclusion criteria, so it falls short of a 5.

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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