一件の競売物件の構造化データを取得. Fetch one auction by case_id (e.g. "bit_00000078922"). Returns case_number, court_code, property_type, sale_base_price_jpy, bid_floor_jpy (= base × 80%), deposit_jpy, bid_period, opening_date, address, photos, and _canonical (cite this).
| Name | Required | Description | Default |
|---|---|---|---|
| case_id | Yes | Auction case id, e.g. "bit_00000078922" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true. Description adds behavioral details like derived fields (bid_floor_jpy = base × 80%) and mentions _canonical for citation, providing value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise: one sentence in Japanese and English, followed by a structured list of returned fields. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description lists key return fields, providing sufficient context for an agent to understand what the tool returns. Coverage is adequate for the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter case_id. Description enhances it with a concrete example and minimal length constraint, clarifying expected format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states 'Fetch one auction by case_id' and lists specific return fields, clearly distinguishing from sibling tools (search, list, find).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides usage context by naming the required parameter and giving an example. While it doesn't explicitly state when not to use, the focus on single-case retrieval and the sibling names imply appropriate 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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Each tool covers a distinct aspect: searching auctions, listing upcoming sales, fetching a single auction's details, looking up court info, and finding practitioners. No two tools overlap in purpose, making selection unambiguous.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., search_auctions, get_auction_detail). The verbs (find, get, list, search) clearly indicate the action, and the nouns are specific to the domain.
Five tools is well-scoped for a read-only data hub covering Japan real-estate auctions. Each tool serves a necessary function without redundancy, and the count is neither too sparse nor overwhelming.
The tool surface covers core operations: search, list, detail, court directory, and practitioner lookup. Minor gaps exist (e.g., no historical auction data or bid results), but the set provides a solid foundation for common investment and legal workflows.