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Mailchimp MCP

Mailchimp MCP

by damientilman
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

get_store_customer

Read-only

Retrieve a single e-commerce store customer's details including email, opt-in status, and order totals. Inspect purchase history and subscription state.

Instructions

Retrieve a single store customer with their email, opt-in status, and order totals.

Use to inspect a customer's purchase history and subscription state. Use list_store_customers to browse and discover customer_ids.

Args: store_id: E-commerce store ID. customer_id: Customer ID. Obtain from list_store_customers.

Returns: JSON with the customer object.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "store_id": "<store_id>",
      "customer_id": "<customer_id>"
    },
    "name": "get_store_customer"
  }
}

Input Schema

NameRequiredDescriptionDefault
accountNo
store_idYes
customer_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.7.1

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds context about returned fields (email, opt-in status, order totals) but does not disclose behavioral details beyond what annotations provide.

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: one sentence for purpose, one for usage, then structured Args and Returns. No redundancy or unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers main purpose and usage, references output as JSON object. But misses the 'account' parameter entirely, leaving a gap in completeness for a simple read tool with three parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must explain all parameters. It covers store_id and customer_id in Args, but omits the 'account' parameter (anyOf string|null) entirely. Incomplete coverage reduces score.

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 the tool retrieves a single store customer with specific fields (email, opt-in status, order totals). Distinguishes from siblings like list_store_customers and create/update/delete variants.

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?

Explicitly says when to use ('inspect a customer's purchase history and subscription state') and references list_store_customers for discovering IDs. Does not mention when not to use, but context from siblings implies alternatives for mutations.

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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