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damientilman

Mailchimp MCP

list_promo_codes

List the redeemable codes assigned to a Mailchimp promo rule, returning each code string, usage count, and redemption URL.

Instructions

List the redeemable codes attached to a promo rule (e.g. 'SUMMER20', 'VIPONLY').

Codes are what customers type at checkout. They redeem the discount defined by the rule. A single rule can have many codes (e.g. one per customer segment).

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

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

Returns: JSON with store_id, promo_rule_id, total_items, and promo_codes array. Each code: id, code (the string customers type), redemption_url, usage_count, enabled, created_at, updated_at.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
store_idYes
promo_rule_idYes
countNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses authentication, rate limits, safe repetition (read-only). Without annotations, description carries full burden and covers key behavioral traits. Could mention response size/pagination more, but sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with summary, context, Args, Returns. Front-loaded sentence effective. Slightly verbose in explanation but concise overall.

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?

Complete description: explains relation between codes and rules, authentication, rate limits, return format, and all parameters. Output schema exists, so return explanation is appropriate.

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?

Input schema has 0% description coverage, but description provides full semantics for all 4 parameters: store_id (E-commerce store ID), promo_rule_id (from list_promo_rules), count (1-1000, default 20), offset (pagination).

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 it lists redeemable codes attached to a promo rule, with examples 'SUMMER20', 'VIPONLY'. Distinguishes from sibling tools like create_promo_code, get_promo_code, delete_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 Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides context: requires API key, max 10 concurrent requests, read-only, and that promo_rule_id comes from list_promo_rules. However, does not explicitly state when not to use or mention alternatives like get_promo_code for a single code.

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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