shopify-mcp

update_variants

Update existing product variants in bulk, modifying price, SKU, barcode, tax status, inventory policy, and option values. Inventory quantity changes require a separate tool.

Instructions

Update one or more existing variants in a single call. Editable fields: price, compareAtPrice (set to null to clear), SKU, barcode, taxable, inventoryPolicy (DENY blocks oversells, CONTINUE allows backorders), and optionValues (e.g. rename a size). Per-variant only; only the fields you provide are written. For inventory quantity changes use set_inventory_quantity — this tool deliberately doesn't accept quantities to keep that audit trail in one place.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "productId": "<productId>",
      "variants": "<variants>"
    },
    "name": "update_variants"
  }
}

Input Schema

Name	Required	Description	Default
`productId`	Yes	Product GID.
`variants`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Apr 25, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Although no annotations exist, the description explains that updates are per-variant and only specified fields are written, and clarifies the meaning of inventoryPolicy values. It does not cover potential side effects or rate limits, but is still sufficiently transparent for a simple update tool.

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 three sentences: first states purpose, second details fields, third gives sibling guidance. It is front-loaded, efficient, and contains no extraneous information.

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?

The description covers editable fields, usage constraints, and points to the related tool. It lacks explicit mention of return value (though standard for updates) and does not mention any prerequisites, but overall provides sufficient context for an AI to use the tool correctly.

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 description adds meaning beyond the schema by listing editable fields (price, compareAtPrice, SKU, etc.), explaining special behavior (compareAtPrice set to null to clear), and providing examples (rename a size for optionValues). This compensates for the schema's 50% description coverage.

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 updates one or more existing variants in a single call, lists editable fields, and distinguishes from the sibling tool set_inventory_quantity for inventory changes.

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

Usage Guidelines5/5

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

Explicitly tells when NOT to use this tool (for inventory quantity changes) and directs to the alternative set_inventory_quantity, providing clear usage boundaries.

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

Deploy Server