AIVA MCP Server

shopify_get_customer

Retrieve Shopify customer details by ID or email to access subscription data, RFM segments, and churn predictions for customer intelligence.

Instructions

Get Shopify customer details.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "shopify_get_customer"
  }
}

Input Schema

Name	Required	Description	Default
`customerId`	No	Shopify customer ID
`email`	No	Customer email (alternative to ID)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jan 24, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It states it 'gets' details, implying a read operation, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or what specific details are returned. This leaves significant gaps for a tool with no annotation coverage.

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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, making it easy to parse quickly without unnecessary elaboration.

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

Completeness2/5

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

Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'details' are returned, how to handle the optional parameters, or any behavioral context. For a read tool with two parameters, this leaves too many gaps for effective agent use.

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 description coverage is 100%, so the schema already documents both parameters ('customerId' and 'email'). The description adds no additional meaning beyond what the schema provides, such as usage examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('Shopify customer details'), making the purpose understandable. It distinguishes from sibling tools like 'aiva_get_customer' by specifying 'Shopify', but doesn't differentiate from other Shopify tools like 'shopify_get_orders' beyond the resource type.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. It doesn't mention when to prefer this over 'aiva_get_customer' or 'aiva_search_customers', nor does it specify prerequisites or exclusions for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server