customer-data-mcp

get_customer_stats

Get summary spend stats for a customer by exact ID: total orders, spend (excluding cancelled/refunded), average order value, and lifetime value.

Instructions

Get summary spend statistics for a single customer by their exact customer ID (e.g. 'CUST-0007'): total order count, total spend (excluding cancelled/refunded orders), average order value, and lifetime value. Use this for questions about how much a specific customer has spent, rather than their raw order list.

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": {
      "customer_id": "<customer_id>"
    },
    "name": "get_customer_stats"
  }
}

Input Schema

Name	Required	Description	Default
`customer_id`	Yes	The exact customer ID, e.g. 'CUST-0007'.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 24, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses a key behavioral detail—total spend excludes cancelled/refunded orders—which is essential to interpret the output. However, it does not cover edge cases like nonexistent customer IDs or permission requirements, but for a read-only summary tool this is acceptable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences: the first lists the purpose and metrics, the second gives usage guidance. The phrase 'e.g. CUST-0007' repeats the schema example, but the description is otherwise efficient and front-loaded.

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

Completeness4/5

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

For a single-parameter read tool with no output schema, the description adequately covers the return values (metrics listed), a hidden behavioral detail (exclusion of cancelled/refunded), and clear usage context. No critical information is missing for correct invocation.

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 100%; the schema already describes customer_id with an example. The description reinforces 'exact' and repeats the same example, adding no new semantic information. It meets the baseline for high schema coverage.

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 verb ('Get'), the resource ('summary spend statistics for a single customer'), and specifies the exact metrics returned (total order count, total spend, average order value, lifetime value). It also explicitly differentiates from raw order lists via the phrase 'rather than their raw order list,' distinguishing it from the sibling get_customer_orders.

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 an explicit when: 'Use this for questions about how much a specific customer has spent,' and a when-not with an alternative: 'rather than their raw order list.' This gives clear guidance without ambiguity.

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

Deploy Server