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Mailchimp MCP

list_store_orders

Retrieve orders from a connected e-commerce store, including totals and fulfillment status. Requires active integration.

Instructions

List orders from a connected e-commerce store with totals and fulfillment status.

Requires an active integration. Use list_store_customers for customer-level aggregates instead.

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

Args: store_id: E-commerce store ID (alphanumeric string). Obtain from list_ecommerce_stores. count: Orders to return (1-1000, default 20). offset: Pagination offset. Use when total_items exceeds count.

Returns: JSON with total_items and orders array. Each: id, customer (email), order_total (float), currency_code (ISO 4217), financial_status, fulfillment_status, processed_at_foreign, lines_count.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
store_idYes
countNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that it's 'Authenticated via API key', 'Max 10 concurrent requests', and 'Read-only, safe to retry', which are key behavioral traits. However, it lacks details on potential error scenarios or rate limits beyond concurrency, so just short of perfect.

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?

The description is well-structured with clear sections for Args and Returns. It is concise, with every sentence adding value, no fluff. It effectively communicates all necessary information without being verbose.

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 the 3 parameters and existence of an output schema (though not provided), the description covers purpose, usage, parameters, return format, and behavioral notes. It also mentions a sibling tool for alternative use, making it contextually complete for an agent to use correctly.

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 coverage is 0%, so the description must explain parameters. It provides clear explanations for all three parameters: store_id includes source info, count includes range and default, offset explains pagination usage. This adds significant meaning beyond the 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 it lists orders from a connected e-commerce store with totals and fulfillment status. It also distinguishes itself from the sibling tool list_store_customers, which is explicitly mentioned, providing clear purpose differentiation.

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?

The description provides explicit when-to-use: 'Requires an active integration' and suggests an alternative: 'Use list_store_customers for customer-level aggregates instead.' This gives clear guidance on context and alternatives.

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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