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codeyogi911

Shopify MCP Server

by codeyogi911

create_product

Create a draft product with title, description, vendor, tags, options, and image URL. Use this as the first step to set up product structure before adding variants and prices.

Instructions

This tool creates a product structure with options (e.g., Color, Size) in DRAFT status. It handles product title, description, vendor, type, tags, and options configuration, but does not create variants with prices. Use this as the first step in product creation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name (title) of the product
tagsNoComma-separated list of tags for the product
vendorNoProduct vendor name
optionsNoProduct options formatted as "OptionName1:Value1,Value2;OptionName2:ValueA,ValueB"
media_urlNoURL of the primary image for the product
descriptionNoProduct description (HTML is supported)
product_typeNoThe category or type of the product

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries full behavioral disclosure. It clearly indicates that the product is created in DRAFT status, handles a defined set of fields, and explicitly states that variants with prices are not created. This is meaningful behavioral context beyond what the parameter names alone would convey.

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?

Two sentences with no filler. The first sentence covers the core purpose and status; the second adds the critical limitation and usage guidance. Every part contributes to the agent's understanding.

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

Completeness3/5

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

The description covers purpose, status, and boundary conditions well, and parameter coverage is complete. However, there is no output schema and the description does not mention what the tool returns (e.g., the created product ID), which is important for 'first step' workflows followed by variant creation. That gap lowers 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds little parameter-level meaning beyond grouping them as 'product title, description, vendor, type, tags, and options configuration,' which duplicates the schema. 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 states a specific verb ('creates') and resource ('product structure with options'), specifies the DRAFT status, and explicitly differentiates itself from variant creation by noting it 'does not create variants with prices.' This allows an agent to distinguish it from sibling tools like add_product_variant without opening schemas.

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 gives clear usage context: 'Use this as the first step in product creation.' It also defines an implicit boundary—no variants or prices—which helps agent know when not to use this tool in favor of add_product_variant. However, it does not explicitly name the alternative tool, so an agent must infer that add_product_variant is the follow-up.

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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