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damientilman

Mailchimp MCP

list_store_customers

Read-only

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

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
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 already indicate readOnlyHint=true (safe read operation). Description adds detail: pagination via offset and total_items, required active integration, and return structure. No contradiction, but could mention idempotency or rate limits. Still adds significant value beyond annotations.

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?

Highly concise: single introductory sentence, followed by usage guidelines, then Args, Returns, and Example. Front-loaded purpose, no redundancy.

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?

Given 4 parameters, output schema, and context signals, the description provides purpose, usage guidelines, parameter details, return format, and an example. Covers all essential information for a list operation.

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?

Schema coverage is 0%, so description bears the burden. It explains store_id, count (with range 1-1000, default 20), and offset (pagination), but does not mention the 'account' parameter. While the output schema provides return structure, the missing parameter weakens completeness.

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 states a specific verb (list) and resource (customers from a connected e-commerce store), and distinguishes itself from sibling tools like list_store_orders (per-order detail) and list_segments.

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?

Explicitly states when to use (analyze customer behavior, identify high-value customers) and when not to (use list_store_orders for order detail), and provides prerequisite: requires active integration and to find store IDs via list_ecommerce_stores.

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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