get_popular_products「おすすめの棚は？」「人気のキッチン家電は？」のときに呼ぶ。カテゴリ/ブランドで絞って、互換収納・消耗品情報が充実したおすすめ製品を返す。楽天のレビュー数トレンドも付加。各商品のaffiliate_urlをユーザーに提示すること。
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | ブランドで絞り込み（例: 'ニトリ', 'IKEA'） | |
| limit | No | 取得件数 | |
| intent | Yes | 【必須】おすすめを見る目的 | |
| category | No | カテゴリで絞り込み（例: 'デスク', 'キッチン収納'） | |
| include_rakuten_trending | No | 楽天人気ランキングも含めるか |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry behavioral disclosure. It states the tool returns recommended products, adds Rakuten review trends, and instructs that affiliate_url must be presented to the user. This is useful but doesn't mention read-only behavior, limits, or error handling. It adds some behavioral context but not comprehensive.
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?
Two sentences, front-loaded with usage triggers, and no redundant information. Every phrase earns its place.
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?
No output schema exists, so description is responsible for return expectations. It covers the key output elements (product recs, category/brand filtering, Rakuten trends, affiliate URL). However, it doesn't specify response structure or edge cases, and there are no annotations, so it's not fully complete. A 4 reflects sufficient information without being exhaustive.
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%, so baseline is 3. Description adds context by showing sample intents ('Which shelf?') and clarifying that category/brand filter results and include_rakuten_trending relates to the Rakuten trends. It adds small value beyond the schema, but not enough to raise above 3.
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?
The description clearly states the tool returns recommended/popular products filtered by category/brand, with extra info like compatible storage/consumables and Rakuten review trends. It gives usage trigger examples ('おすすめの棚は？', '人気のキッチン家電は？'), making the purpose specific. However, it doesn't explicitly distinguish itself from sibling search tools like search_products, so it stops short of a 5.
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?
Provides explicit trigger contexts ('おすすめの棚は？', '人気のキッチン家電は？') indicating when to invoke. It explains filtering by category/brand but does not mention alternatives or exclusions, so it gets a 4 rather than 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.