Skip to main content
Glama
tzangms

shopline-mcp

by tzangms

bulk_delete_product_reviews

Batch delete multiple product reviews permanently. Clears test data or removes violating reviews in bulk.

Instructions

[WRITE] 批次刪除多筆商品評論。

【用途】 一次性永久刪除多筆評論,適用於批次清除測試資料或大量違規評論。

【呼叫的 Shopline API】

  • DELETE /v1/product_review_comments

【回傳結構】 dict 含 success: bool, resource_id: str, message: str, deleted_count: int。

【副作用】

  • 永久刪除多筆評論記錄,不可復原

  • 相關商品的評論總數與平均評分將同步更新

  • 部分 id 若不存在,整批可能失敗或僅失敗該筆(視 API 實作而定)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
comment_idsYes要刪除的評論 ID 列表,例如 ["C001", "C002", "C003"]
Behavior5/5

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 clearly states that deletions are permanent and irreversible, that product review counts and average ratings will be updated, and that partial failures may occur depending on API implementation. This is highly transparent for a destructive operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with labeled sections: purpose, API call, return structure, and side effects. It is concise, with every sentence providing meaningful information. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter bulk delete tool with no annotations and no output schema, the description is complete. It covers purpose, API endpoint, return structure, and side effects, giving the agent all necessary context to invoke the tool correctly. Sibling tools are not referenced but not needed for completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage for the single parameter comment_ids, including an example. The description does not add extra semantic detail beyond the schema, which is acceptable since the schema already provides sufficient meaning. The baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: batch deleting multiple product reviews ('批次刪除多筆商品評論'). It specifies the action (delete), resource (product reviews), and scope (batch/multiple), distinguishing it from the single-delete sibling tool. The use case for batch clearing test data or violating reviews adds specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context by stating suitable scenarios (batch clearing test data or large numbers of violating reviews). However, it does not explicitly mention alternatives like delete_product_review for single deletions or when NOT to use this tool, leaving some implicit guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tzangms/shoplinemcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server