ai-furniture-hub

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns products with compatibility/storage information and Rakuten review trends, and that affiliate URLs should be presented to users. However, it doesn't disclose important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or response format details. The description adds some context but leaves significant gaps.

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 appropriately concise with three sentences that each serve a purpose: when to use the tool, what it does, and what to do with the results. It's front-loaded with usage context. While efficient, it could be slightly more structured by separating behavioral details from usage guidelines.

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?

Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description provides good usage context but lacks important behavioral details. It explains when to use the tool and what it returns, but doesn't cover response format, error handling, or operational constraints. For a tool with no annotations or output schema, more completeness would be expected.

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 description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description mentions filtering by category/brand and including Rakuten trends, which aligns with the schema but doesn't add meaningful semantic information beyond what's already in the parameter descriptions. The baseline of 3 is appropriate when the schema does the heavy lifting.

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 return recommended products filtered by category/brand with compatibility/storage information and Rakuten review trends. It specifies the verb ('返す' - return) and resource ('おすすめ製品' - recommended products), and distinguishes from siblings like search_products or get_curated_sets by focusing on popularity/recommendations rather than general search or curated sets.

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 this tool: in response to questions like 'おすすめの棚は？' (What shelves do you recommend?) or '人気のキッチン家電は？' (What are popular kitchen appliances?). It also implies when not to use it by specifying its focus on popular/recommended products with compatibility information, suggesting alternatives like search_products for general searches or get_product_detail for individual product details.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.