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damientilman

Mailchimp MCP

list_store_customers

Retrieve customer data from a connected e-commerce store, including order counts, total spend, and opt-in status, to analyze purchasing behavior or identify high-value customers.

Instructions

List customers from a connected e-commerce store with order counts, total spend, and opt-in status.

Use to analyze customer purchasing behavior or identify high-value customers. Requires an active e-commerce integration. Use list_ecommerce_stores to find store IDs. Use list_store_orders for per-order detail instead of customer-level aggregates.

Authenticated via API key. Subject to Mailchimp API rate limits (max 10 concurrent requests). Read-only, safe to retry.

Args: store_id: The e-commerce store ID. Obtain from list_ecommerce_stores. count: Number of customers to return (1-1000, default 20). offset: Pagination offset. Use when total_items exceeds count.

Returns: JSON with total_items and customers array. Each customer: id (string), email_address, first_name, last_name, orders_count (int), total_spent (float, in store currency), opt_in_status (boolean), created_at (ISO 8601).

Example: list_store_customers(store_id="store123", count=50) -> {"total_items": 500, "customers": [{"email_address": "jane@co.com", "orders_count": 5, "total_spent": 299.95, ...}]}

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 fully covers behavioral traits: authenticated via API key, subject to rate limits (10 concurrent), read-only and safe to retry. Also explains pagination via offset.

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?

Concise yet comprehensive: one-line summary, usage context, behavioral notes, parameter descriptions, return format, and example. No wasted words, well-organized.

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?

Despite no output schema in definition, description fully specifies return structure (fields, types, example). Covers pagination and all necessary context for a list tool with 3 parameters.

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 explains each parameter: store_id from list_ecommerce_stores, count range (1-1000, default 20), offset for pagination. Adds significant value beyond 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 the tool lists customers with order counts, total spend, and opt-in status. It distinguishes from siblings by explicitly mentioning list_ecommerce_stores for store IDs and list_store_orders for per-order detail.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

Provides explicit guidance: use to analyze customer behavior or identify high-value customers, requires active integration, directs to related tools for prerequisites or alternatives. Also notes read-only and safe to retry.

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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