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damientilman

Mailchimp MCP

list_promo_rules

Retrieve discount/promo rules for a store, showing type, amount, target, and validity dates.

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.

Authenticated via API key. Max 10 concurrent requests. Read-only, safe to retry.

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
store_idYes
countNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description carries full burden. Explicitly states 'Read-only, safe to retry', authentication via API key, max 10 concurrent requests. Fully discloses behavioral traits.

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: purpose, definition, auth/limits, args, returns. Every sentence adds value. No fluff.

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?

Describes return format in detail (JSON with store_id, total_items, promo_rules array with fields). With output schema present, this is sufficient and complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions are absent (0% coverage). Description adds meaning: store_id obtained from list_ecommerce_stores, count range 1-1000 with default 20, offset for pagination. Compensates fully.

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?

Clearly states the tool lists discount/promo rules, elaborates on what a promo rule is, and differentiates from sibling tools like create_promo_rule, delete_promo_rule, get_promo_rule, etc.

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 guidance on authentication, concurrency limits, and read-only nature. Instructs to obtain store_id from list_ecommerce_stores. Does not explicitly exclude alternatives but context with siblings makes usage clear.

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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