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

With no annotations, the description carries the full burden. It discloses meaningful behavior: results are split into required and recommended groups, each item comes with Rakuten search results for immediate suggestion, depth=2 expands to 'related of related', and required=true items must be communicated to the user. This goes beyond a simple verb and gives actionable behavioral rules.

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 concise and well-structured: it opens with the primary use case, then explains the output split, the Rakuten integration, the depth behavior, and the mandatory instruction about required items. Every sentence adds value, and the most important information is front-loaded.

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 has 5 parameters and no output schema, the description covers the main aspects: when to use, what it returns, and a key rule. However, it lacks details on how product_id and keyword interact (e.g., which takes precedence, whether both can be provided), and it doesn't describe the response structure beyond the required/recommended split. This is a minor gap but not critical.

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 some context to parameters (e.g., depth=2 expansion, Rakuten search results related to include_rakuten), but essentially restates what the schema already documents. It doesn't clarify the precedence or mutual exclusivity of product_id and keyword, so it doesn't elevate above 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 suggest complementary items (accessories, protective materials, parts) after finding a product with search_products. It distinguishes itself from siblings by focusing on the 'what else do you need?' use case and explicitly separates required vs recommended 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 provides clear usage context: call it after using search_products to find a product. It doesn't explicitly name alternatives, but the context ('他に何が必要？') and the reference to search_products make the intended workflow clear. It implies not to call it before product discovery.

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