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damientilman

Mailchimp MCP

list_promo_rules

Read-only

Retrieve discount and promo rules (fixed amount, percentage, free shipping) for an e-commerce store, with details like type, amount, and status.

Instructions

List discount/promo rules configured for a store (fixed amount, percentage, free shipping).

A promo rule defines the discount mechanic (e.g. '20% off entire order'). Codes that customers redeem are attached to rules via list_promo_codes / create_promo_code.

Args: store_id: E-commerce store ID. Obtain from list_ecommerce_stores. count: Number of rules to return (1-1000, default 20). offset: Pagination offset.

Returns: JSON with store_id, total_items, and promo_rules array. Each rule: id, title, description, amount, type ('fixed' | 'percentage'), target ('per_item' | 'total' | 'shipping'), enabled (bool), starts_at, ends_at, created_at, updated_at.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
offsetNo
accountNo
store_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations declare readOnlyHint=true and destructiveHint=false; description adds pagination details, default count, and return format, which are useful beyond annotations. No contradictions.

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?

Well-structured with clear sections (purpose, context, args, returns). Front-loaded and concise without extraneous text.

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

Completeness5/5

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

Fully covers parameter usage, pagination, and return structure. Output schema exists, so return explanation is sufficient. No gaps given the tool's complexity.

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?

With 0% schema coverage, description adds meaning for store_id (source), count (range/default), and offset but omits explanation for the 'account' parameter, leaving it undocumented.

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 lists promo rules for a store with specific examples of discount types (fixed amount, percentage, free shipping), distinguishing it from related tools like create_promo_rule or get_promo_rule.

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?

Provides context that codes are attached to rules via list_promo_codes/create_promo_code, implying when to use this tool. Lacks explicit when-not or alternatives but is clear enough for an agent to decide.

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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