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tzangms

shopline-mcp

by tzangms

create_product

Create a new product in your Shopline store by providing complete product data including title, description, price, and SKU. The product becomes immediately visible in the store backend.

Instructions

[WRITE] 建立新商品。

【用途】 在 Shopline 商店中建立一筆新的商品記錄。product_data 為完整的商品 body, 應依 Shopline Open API 規格組裝(含名稱、描述、售價、SKU 等)。

【呼叫的 Shopline API】

  • POST /v1/products

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

【副作用】

  • 商品立即在商店後台可見

  • 若 SKU 或 barcode 重複,API 可能回傳錯誤

  • 新商品預設狀態取決於 product_data 內的 status 欄位

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_dataYes完整商品建立資料,依 Shopline API 規格傳入(含 title、description、price 等欄位)
Behavior5/5

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

With no annotations provided, the description carries the full transparency burden. It discloses the write nature ([WRITE]), side effects (immediate visibility, duplicate SKU/barcode errors), and the return structure, offering substantial behavioral insight beyond the structured schema.

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 sections (purpose, API call, return structure, side effects) and is front-loaded with the core action. Each sentence adds value, and the format is easy to scan.

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?

The description fully addresses the tool's complexity by including the API endpoint, return structure, and side effects, including duplicate error behavior. Since no output schema exists, the explicit return structure is especially valuable for an AI agent.

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 schema description already covers the single parameter with 100% coverage, explaining it should be a complete product body per the Shopline API spec. The tool description adds emphasis on completeness and mentions SKU, but this largely overlaps with the schema and does not provide significant additional meaning.

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 explicitly states the tool creates a new product record in Shopline, using the verb '建立' (create) and the resource '商品' (product). It further distinguishes from sibling tools like update_product and delete_product by specifying the POST /v1/products endpoint and emphasizing creation of a new record.

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 clearly indicates when to use this tool: to create a new product. It also provides practical guidance on assembling product_data according to the Shopline Open API spec. While it does not explicitly mention alternatives, the context is clear and no exclusions are necessary.

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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