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家具・家電・ガジェット商品を検索

search_products

ユーザーが「棚が欲しい」「Dysonのドライヤー」「幅40cmに入るもの」と言ったときに呼ぶ。31カテゴリ・80+ブランドのカタログをキーワード・サイズ(mm)・価格・色・ブランドで横断検索。結果にrelated_items_hintがある場合はget_related_itemsで付属品チェーンを取得できる。buy_guideがある場合はbest_for/avoid_ifをユーザーに伝えて購入判断を助けること。seasonal_hints/active_salesがある場合はセール情報を伝えること。色はエイリアス対応(白→ホワイト/アイボリー等)。各商品のaffiliate_urlをユーザーに提示すること。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNoブランド(例:ニトリ、IKEA、Dyson、Panasonic)
colorNo色(例:ホワイト、白、ブラウン、木目)。エイリアス対応:白→ホワイト/アイボリー等
intentYes【必須】検索目的
keywordNoキーワード(商品名・ブランド・タグで部分一致、スペース区切りでAND検索)
categoryNoカテゴリ(例:デスク、美容家電、スマートホーム)
price_maxNo価格の上限(円)
price_minNo価格の下限(円)
depth_mm_maxNo奥行きの最大値(mm)
depth_mm_minNo奥行きの最小値(mm)
width_mm_maxNo幅の最大値(mm)
width_mm_minNo幅の最小値(mm)
height_mm_maxNo高さの最大値(mm)
height_mm_minNo高さの最小値(mm)
in_stock_onlyNo在庫ありのみ(デフォルト:true)

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are present, so the description carries the full burden. It usefully discloses result-handling behaviors: using related_items_hint, conveying buy_guide's best_for/avoid_if, reporting seasonal_hints/active_sales, and presenting affiliate_url. It does not mention pagination, sorting, or whether the operation is read-only, but '横断検索' implies query-only.

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?

The description is a single well-structured paragraph. Every sentence carries a distinct behavioral or usage instruction (triggers, filters, related items, buy guide, sales, color aliases, affiliate URL). It is longer than minimal but not bloated, and the trigger examples are front-loaded for quick scanning.

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?

With no output schema, the description compensates by hinting at result fields (related_items_hint, buy_guide, seasonal_hints, active_sales, affiliate_url) and dictating agent actions for each. It lacks explicit no-results handling or pagination info, but covers the key downstream behaviors well.

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?

Schema coverage is 100% with detailed descriptions for all 14 parameters. The description adds some context by listing filter dimensions (keyword, size, price, color, brand) and trigger phrases that map to intent and size params, but this is largely redundant with the schema. Baseline 3 is appropriate.

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 opens with concrete user utterances ('棚が欲しい', 'Dysonのドライヤー', '幅40cmに入るもの') and explicitly states it cross-searches a catalog of 31 categories and 80+ brands by keyword, size, price, color, and brand. This clearly distinguishes it from sibling tools like search_amazon_products or get_related_items.

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?

Provides explicit trigger phrases and scope (internal catalog, 31 categories, 80+ brands). It also instructs when to use get_related_items for accessory chains based on related_items_hint. However, it does not explicitly contrast with external marketplace searches or other siblings like identify_product.

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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TDQS

A3.9/5.0
Disambiguation3/5

Multiple tools handle product discovery (search_products, search_rakuten_products, search_amazon_products, suggest_by_space, find_replacement), and their boundaries require careful description reading. diagnose_ai_visibility is completely unrelated to furniture, adding confusion. Most tools are distinct, but the search cluster is ambiguous.

Naming Consistency4/5

Tool names consistently use snake_case with verb prefixes (get_, search_, find_, list_, etc.). While some verbs overlap (search vs find), the pattern is predictable and no mixed styles exist.

Tool Count4/5

18 tools is on the higher side but reasonable for a rich domain. However, several search variants and the unrelated diagnose_ai_visibility tool could be consolidated, making it slightly bloated.

Completeness4/5

The furniture hub covers the full user journey: search, detail, compare, related, photo measurement, space planning, storage, replacements, and even demand analysis. Missing features like direct purchase are handled via affiliate links. The unrelated AI visibility tool doesn't create a gap in furniture functionality.

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