廃番・旧型の後継・代替品を探す
find_replacement「この型番が売ってない」「生産終了した棚の代わり」のときに呼ぶ。カタログの後継候補(successors)と楽天の「後継」「新型」検索結果を返す。最終確認はメーカー公式で。楽天候補のaffiliate_urlをユーザーに提示すること。
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | 型番または商品名・特徴テキスト | |
| intent | Yes | 【必須】なぜ代替が必要か |
find_replacement「この型番が売ってない」「生産終了した棚の代わり」のときに呼ぶ。カタログの後継候補(successors)と楽天の「後継」「新型」検索結果を返す。最終確認はメーカー公式で。楽天候補のaffiliate_urlをユーザーに提示すること。
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | 型番または商品名・特徴テキスト | |
| intent | Yes | 【必須】なぜ代替が必要か |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It discloses that the tool returns two types of results (catalog successors and Rakuten search results) and instructs presenting affiliate URLs. However, it does not mention any potential side effects, authentication needs, or failure scenarios, which would be useful for a read/search tool without annotation support.
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?
The description is front-loaded with a clear trigger phrase, followed by output details and a critical instruction about affiliate URLs. Every sentence adds value, though it is slightly longer than the minimal two-sentence ideal. No redundancy.
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?
With no output schema and no annotations, the description should explain return values and handling. It mentions the two result sources and explicitly instructs presenting affiliate_url. However, it leaves out details like result format, limits, or error handling, which would make the tool easier to use correctly.
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?
The schema already provides descriptions for both required parameters (query and intent), covering model number/product name and the reason for replacement. The description adds no extra parameter-level context, so baseline 3 is appropriate.
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 defines the tool's role: finding successor/alternative products for discontinued or unavailable model numbers. It explicitly specifies it returns catalog successors and Rakuten search results, which distinguishes it from generic product search siblings like search_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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to call the tool: when a model number is not available or a shelf is discontinued. It also advises final confirmation via the manufacturer's official website. It does not mention when not to use it or alternative tools, but the trigger scenarios are clear.
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.