get_product_review_detail

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 transparently lists the associated Shopline API (GET /v1/product_review_comments/{comment_id}) and details the return structure, implying a read-only operation without mutation. However, it does not mention authentication requirements, rate limits, or error behavior, leaving a moderate gap in transparency.

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 well-structured with clear sections (用途, 呼叫的 Shopline API, 回傳結構) and a one-sentence summary upfront. It is concise yet packed with necessary information—purpose, use cases, API endpoint, and return fields—without any redundant content. Every sentence earns its place.

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?

For a low-complexity tool with one parameter, no annotations, and no output schema, this description is remarkably complete. It explains what the tool does, when to use it, which API it calls, and exactly what the returned dictionary contains. This is sufficient for an agent to select and correctly invoke the tool with minimal ambiguity.

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 input schema already describes comment_id as 評價 ID, achieving 100% parameter coverage. The description adds a small but useful note that the ID comes from the id field returned by list_product_reviews, which helps agents trace the data provenance. This extra context slightly elevates the description beyond the schema baseline, but not substantially.

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 opens with a clear, specific statement: 取得單一商品評價的完整內容，包含圖片與店家回覆 (Get full content of a single product review, including images and merchant reply). This precisely identifies the resource (single product review detail) and the action (get), distinguishing it from sibling tools like list_product_reviews, update_product_review, and delete_product_review.

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 includes a dedicated section listing suitable use cases: 客服處理評價問題、追蹤店家回應進度，或人工審核評價內容 (customer service handling review issues, tracking merchant response progress, or manually auditing review content). This provides clear usage context, but it does not explicitly mention alternatives or exclusion criteria, which keeps it a notch below a perfect score.

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