zobrx

Get P&L / money summary

get_pnl_summary

Read-only

Amazon settlement-based money summary over a window: revenue, marketplace fees, refunds, TCS/TDS, net amount Amazon settled, FBA reimbursements, net to seller, plus order/return/reimbursement counts. Amounts are in major currency units.

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": "get_pnl_summary"
  }
}

Input Schema

Name	Required	Description	Default
`days`	No	Lookback window in days (default 30).

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJun 19, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": false,
+  "properties": {},
+  "type": "object"
+}

Changed1 schema field changedJun 15, 2026
- removedInput schema / $schema
```
Removed value: -"http://json-schema.org/draft-07/schema#"
```
First observedJun 15, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds valuable behavioral context by specifying the data source (Amazon settlements), the included line items, and that amounts are in major currency units. This enriches understanding beyond annotations without any contradiction.

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, well-organized sentence that front-loads the core purpose ('Amazon settlement-based money summary') and then efficiently lists the included components. Every phrase adds value, and there is no redundant or filler text.

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?

With an output schema present, the description does not need to detail return structure. It covers data source, window concept, included components, and units—sufficient for a straightforward summary tool with one optional parameter. No critical context is missing.

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% for the single 'days' parameter, which is fully described in the schema as a lookback window with default 30. The description's phrase 'over a window' aligns but adds no new semantic detail beyond the schema. Baseline of 3 is appropriate.

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 and resource: 'Amazon settlement-based money summary over a window.' It explicitly enumerates the financial components included (revenue, fees, refunds, TCS/TDS, net amounts, reimbursements), which clearly distinguishes it from sibling summary tools like get_payments_summary or get_reimbursements_summary.

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?

The description provides clear context for when to use the tool: when an Amazon settlement-based money summary over a window is needed. It does not explicitly name alternatives or exclusion scenarios, but the context is unambiguous enough to guide selection among the many summary-focused siblings.

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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