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damientilman

Mailchimp MCP

create_store_product_variant

Create a new variant (SKU, size, color) for an existing product in your store. Use for custom or headless storefronts that need manual variant management.

Instructions

Add a new variant to an existing product in a store.

Use to represent a distinct SKU, size, or color of a product. Many stores sync variants automatically via Shopify or WooCommerce integrations; these manual writes suit custom or headless storefronts.

Args: store_id: E-commerce store ID. product_id: Product ID to add the variant to. variant_id: Client-supplied unique ID for the new variant. title: Variant title (e.g. 'Large / Blue'). additional_fields: Optional dict of extra documented fields (e.g. url, sku, price, inventory_quantity, image_url) merged into the request body.

Returns: JSON with the created variant object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
accountNo
store_idYes
product_idYes
variant_idYes
additional_fieldsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations indicate non-read-only, non-idempotent, non-destructive. The description adds integration context but does not disclose much beyond what annotations imply (e.g., no mention of uniqueness constraints or potential errors). No contradiction.

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 succinct, well-structured with a summary, usage note, and parameter list. Every sentence adds value.

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?

With an output schema and annotations, the description adequately covers purpose, usage, parameters, and return format. Some missing details about prerequisites or errors, but generally complete.

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?

Schema coverage is 0%, but the description explains all parameters except 'account', providing examples for 'title' and 'additional_fields'. This adds meaning beyond the bare 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 action ('Add a new variant'), the resource ('to an existing product in a store'), and provides context ('distinct SKU, size, or color'). It distinguishes from sibling tools like create_store_product and update_store_product_variant.

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 advises that many stores sync variants automatically via Shopify or WooCommerce, positioning this tool for custom or headless storefronts. This provides clear usage context, though it could explicitly state when not to use it.

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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