Skip to main content
Glama

create_product

Creates a new product in a Shopline store by providing product data such as title, description, price, and SKU. The product becomes immediately available 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)
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the product is immediately visible, potential errors on duplicates, and default status behavior. It also marks it as a write operation with '[WRITE]'. This provides adequate behavioral context for an agent to understand side effects.

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 into clear sections (用途, API call, return structure, side effects) with a front-loaded purpose. Every sentence serves a purpose, and it is appropriately sized for the tool's complexity.

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

Completeness4/5

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

Given the lack of output schema, the description provides the return structure and side effects. The single parameter with additionalProperties is explained with field suggestions. It could be more explicit about required fields within product_data, but the reference to the API spec is acceptable.

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

Parameters4/5

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

The input schema has 100% description coverage with a single parameter 'product_data'. The description adds value by instructing that the object should be assembled according to the Shopline API spec and listing example fields (name, description, price, SKU). It also describes the return structure, which is absent from the schema.

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 verb '建立' (create) and resource '新商品' (new product), and the title 'create_product' distinguishes it from siblings like 'create_addon_product' and 'update_product'.

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 explains that the tool is for creating a new product in the Shopline store, with details on the Shopline API endpoint and error conditions for duplicate SKU/barcode. However, it does not explicitly contrast with alternative tools or provide when-not-to-use guidance, but the purpose is clear enough for basic usage.

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/asgard-ai-platform/mcp-shopline'

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