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

There are no annotations, so the description carries full behavioral disclosure. It explains conditional behavior (catalog matches add inner dimensions, compatible storage, buy_guide), how buy_guide's decision_hint is reflected in the comparison recommendation, and instructs presenting affiliate_url to the user. This goes beyond a simple statement and gives actionable behavioral context.

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 compact and front-loaded, starting with the primary usage condition and example. Each sentence provides distinct information (output, conditional add-ons, recommendation logic, user instruction) without fluff or redundancy. The structure is logical and easy to parse for an AI agent.

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 no output schema and no annotations, the description needs to explain return values and behaviors. It does so: describing the parallel comparison table, default and conditional fields, and the affiliate_url presentation directive. It covers the tool's main behavior and expectations sufficiently for an agent to invoke and interpret results.

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 baseline is 3. The description does not add substantial meaning to the parameters beyond the schema; it mentions default comparison aspects (price, size, reviews, load capacity) but does not elaborate on the intent, keywords, or compare_aspects parameters themselves. It neither compensates nor hurts.

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: comparing 2-5 products, with a concrete example ('NクリックとKALLAXどっちがいい？'). It names the specific resources compared (price, size, reviews, load capacity), and the title reinforces '並列比較' (parallel comparison). This distinguishes it from siblings like get_product_detail (single product) and search_products (searching).

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 when-to-use guidance: '2〜5製品を比較するときに呼ぶ' (call when comparing 2-5 products), with an example query. However, it does not explicitly mention when not to use this tool or point to alternatives, so it lacks explicit exclusions.

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