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damientilman

Mailchimp MCP

create_store_product

Add a new product to your Mailchimp e-commerce store with required variants and optional fields like price and images.

Instructions

Create a product in a store, including at least one variant.

Every product needs one or more variants; a simple product still has a single default variant. Many stores sync products automatically via Shopify or WooCommerce integrations; these manual writes suit custom or headless storefronts.

Args: store_id: E-commerce store ID. product_id: Client-supplied unique ID for the new product. title: Product title shown in emails and the Mailchimp UI. variants: List of variant dicts (minimum 1), each with at least an id and title (e.g. [{"id": "v1", "title": "Default", "price": 19.99}]). additional_fields: Optional dict of extra documented fields (e.g. handle, url, description, type, vendor, image_url, images) merged into the request body.

Returns: JSON with the created product object.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
accountNo
store_idYes
variantsYes
product_idYes
additional_fieldsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description adds that the tool requires at least one variant and is for manual writes, which aligns with the annotations (non-readOnly, non-idempotent, non-destructive). However, it does not disclose potential side effects like overwriting an existing product_id or required permissions, leaving gaps despite basic annotation coverage.

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 a brief intro, Args list, and Returns note. It is front-loaded, concise, and contains no unnecessary words or repetition.

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 the core functionality, variant requirement, integration context, and parameter details. With an output schema present, return value explanation is sufficient. It lacks error cases and permission requirements but is fairly complete for a creation tool.

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?

With 0% schema description coverage, the description's Args section adds significant meaning for each parameter except 'account'. It explains the format and requirements for 'variants' and 'additional_fields', providing crucial context 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 it creates a product in a store and requires at least one variant, distinguishing it from sibling tools like create_store_product_variant. It also provides context about automatic vs manual storefronts, clarifying its specific use.

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 products need variants and that manual writes are for custom/headless storefronts, providing context for when to use this tool. However, it does not explicitly exclude alternative scenarios or mention 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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