update_product_review

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

With no annotations provided, the description carries full burden. It discloses that changes take effect immediately, that changing the rating will sync the product's average rating, and that the operation is irreversible but can be overridden by calling again. It also provides the return structure (dict with success, resource_id, message). This is rich, non-obvious behavioral context far beyond a simple 'update' statement.

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 (purpose), 呼叫的 Shopline API (called API), 回傳結構 (return structure), and 副作用 (side effects). Every section provides necessary information without redundancy. The opening line is front-loaded with the action and resource. No wasted words.

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 params, no output schema) and the description covers purpose, usage context, API endpoint, return structure, and side effects. This is near-complete for an update operation. It lacks mention of error cases or prerequisites (e.g., what happens if comment_id does not exist), but given the tool's straightforward nature and the presence of return structure, the description is sufficiently complete.

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%: both comment_id and review_data have meaningful descriptions, with review_data including an example object. The tool description adds minimal extra parameter info, only hinting at updatable fields in the purpose section. Baseline of 3 is appropriate since the schema already does the heavy lifting and the description does not significantly enhance parameter understanding.

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 '更新單筆商品評論' (update single product review), clearly stating the verb and resource. It further specifies fields like content, moderation status, and rating, and names the exact API endpoint PATCH /v1/product_review_comments/{comment_id}. This clearly distinguishes it from sibling tools like create_product_review or bulk_update_product_reviews.

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 states an explicit use case: '適用於客服審核或編輯不當評論' (suitable for customer service moderation or editing inappropriate reviews), providing clear context. However, it does not explicitly contrast with alternatives such as bulk_update_product_reviews or delete_product_review, nor does it state when not to use this tool. Thus it has clear context but lacks exclusions/alternatives.

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