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Mailchimp MCP

create_promo_rule

Create a discount rule in a Mailchimp store, specifying amount, type (fixed or percentage), and target (per item, total, or shipping). Attach promo codes separately.

Instructions

Create a promo rule (discount mechanic) in a store. Attach codes to it afterwards via create_promo_code.

promo_rule_id is client-supplied. Common patterns: amount=20 + type='percentage' + target='total' for '20% off entire order'; amount=5 + type='fixed' + target='shipping' for '$5 off shipping'.

Authenticated via API key. Max 10 concurrent requests. Respects read-only and dry-run modes.

Args: store_id: E-commerce store ID. promo_rule_id: Client-supplied unique ID for the rule. description: Internal description shown in Mailchimp UI. amount: Discount value. For type='percentage', a value between 0 and 100. type: 'fixed' for absolute amount or 'percentage' for percent off. target: 'per_item' (each item), 'total' (whole order), or 'shipping' (shipping cost only). enabled: Whether the rule is active. Default true. title: Optional public title. starts_at: Optional ISO 8601 start datetime (rule inactive before this). ends_at: Optional ISO 8601 end datetime (rule inactive after this).

Returns: JSON with id, title, description, amount, type, target, enabled, created_at.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
store_idYes
promo_rule_idYes
descriptionYes
amountYes
typeYes
targetYes
enabledNo
titleNo
starts_atNo
ends_atNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Without annotations, description carries full burden. Discloses authentication, rate limiting, modes, and client-supplied promo_rule_id. Does not mention idempotency or error behavior, but covers key behavioral aspects well.

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?

Efficiently structured: purpose first, then usage notes, common patterns, Args list, and Returns. No redundant sentences; all info earns its place despite 10 parameters.

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?

Provides input examples, auth/rate limits, and return description. Lacks information on error handling or duplicate id behavior, but given complexity (10 params, no annotations), it is nearly 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?

With 0% schema description coverage, the description fully compensates by explaining each parameter's meaning, including value ranges (e.g., amount 0-100 for percentage), options for type and target, defaults, and formatting for datetime fields.

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 promo rule (discount mechanic) in a store and mentions attaching codes via create_promo_code, distinguishing it from sibling tools like update_promo_rule and create_promo_code.

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 explicit guidelines: authentication via API key, max 10 concurrent requests, respects read-only/dry-run modes, and suggests chaining with create_promo_code. Lacks explicit when-not-to-use scenario (e.g., when to update instead of create), but overall strong.

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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