list_categoriesBrowse all 31 furniture categories to discover available products and brands, then select a category to view specific items.
「何が検索できる？」「どんなカテゴリがある？」のときに呼ぶ入口ツール。全31カテゴリと製品数・取扱ブランドを返す。カテゴリ名指定でそのカテゴリの製品一覧も取得可能。まずこのツールでカテゴリを把握→ユーザーに提示→選んだカテゴリでsearch_productsに進む。
| Name | Required | Description | Default |
|---|---|---|---|
| intent | Yes | 【必須】カテゴリを見る目的 | |
| category_filter | No | 特定カテゴリに絞る（例: 'キッチン収納', 'デスク'） |
Changes observed during successful MCP inspections.
v6.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the output type (all 31 categories, product counts, brands) and the filtering capability, but does not explicitly state that it is a read-only operation, nor does it mention any limitations, error behavior, or rate limits. For a list tool, this is adequate but not rich.
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 three short sentences, front-loaded with the main purpose and usage trigger. Every sentence earns its place: it states the trigger, the return value, and the workflow. No redundant or vague information.
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?
The tool is simple (2 parameters, no output schema), and the description covers the key context: what it returns, how to use it, and how it fits into the larger workflow. It does not explain the exact structure of the returned data, but since no output schema exists, the description does its job adequately.
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 the baseline is 3. The description adds meaning by explaining that the category_filter parameter allows retrieving a product list for a specific category. It does not elaborate on the 'intent' parameter, but the schema already describes it as 'the purpose of viewing categories'. The added value slightly exceeds baseline.
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's purpose: to list all 31 categories with product counts and brands, and to serve as an entry point for category exploration. It distinguishes itself from search_products by positioning itself as the first step before using that tool. The verb '返す' (return) and specific resources (categories, counts, brands) make it unambiguous.
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 use it: when the user asks what can be searched or what categories exist. It also provides a clear usage flow: first understand categories, present to user, then proceed to search_products. However, it does not explicitly mention when not to use it or alternative tools beyond search_products, which would make it a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.