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tzangms

shopline-mcp

by tzangms

adjust_customer_member_points

Add or deduct customer member points for manual adjustments, campaign rewards, or dispute compensation. Updates balance instantly and records history.

Instructions

[WRITE] 調整客戶會員點數。

【用途】 增加或扣除客戶會員點數,常用於手動補點、活動贈點、客訴補償等場景。

【呼叫的 Shopline API】

  • PUT /v1/customers/{customer_id}/member_points

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

【副作用】

  • 客戶點數餘額立即變動

  • 異動紀錄會寫入客戶的點數歷史(可透過 get_customer_profile 查看)

  • 扣除後如點數不足,API 可能回傳錯誤

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo調整備註/原因
pointsYes調整點數(正數=增加,負數=扣除)
customer_idYes客戶內部 ID
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. It discloses side effects (immediate balance change, points history recording), an error condition (deduction may fail if insufficient points), and the return structure. This is rich behavioral context well beyond a basic operation statement.

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 clear section headers (用途, API, 回傳結構, 副作用), front-loaded with the [WRITE] flag and purpose. Every section adds value and the length is justified for a write operation with no annotations.

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 write tool with no annotations and no output schema, the description covers purpose, usage scenarios, the underlying API endpoint, return structure, and side effects. It even references a sibling tool for verification. It is fully sufficient for an agent to invoke correctly.

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% description coverage for all three parameters, including the sign convention for points. The description does not add additional parameter-level meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 opens with '[WRITE]' and explicitly states the tool's function: increasing or deducting customer member points, with concrete use cases (manual top-up, promotional gifts, complaint compensation). This clearly identifies the action and resource, distinguishing it from sibling read-only tools like list_member_point_rules.

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 specifies when to use the tool: '常用於手動補點、活動贈點、客訴補償等場景' (commonly used for manual point additions, promotions, and complaint compensation). It provides clear context but does not explicitly state when not to use it or name alternative tools, so it falls short of a 5.

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

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