Amazon Seller MCP Server — AgentXray

Profit summary (measured)

get_profit_summary

Read-onlyIdempotent

Real profit and loss for the connected Amazon seller account over a window: revenue, Amazon fees, COGS, net profit and margin, grouped per currency. Numbers come from posted Amazon fee events joined to the seller's own orders and saved product costs, so fees include the consumption tax Amazon actually charged (such as AU GST). Use this for 'what did I actually make', 'was last month profitable', 'what are my fees'.

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_profit_summary"
  }
}

Input Schema

Name	Required	Description
`date`	No	ONE calendar day instead of a window: 'yesterday', 'today' or YYYY-MM-DD (within the last 365 days). The day is the STORE's own day in its marketplace's timezone. Use it for 'how was yesterday' or a named date; do not also pass days.
`days`	No	Window in days, 1-365 (default 30).
`end_date`	No	Last day of the custom range, YYYY-MM-DD (inclusive).
`start_date`	No	First day of a custom range, YYYY-MM-DD on the STORE's calendar (inclusive). Pass with end_date for 'September', 'between X and Y' or any past period; it replaces days. At most 730 days.
`marketplace_id`	No	Optional marketplace filter (e.g. ATVPDKIKX0DER).

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A4.1/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), yet the description adds substantial context beyond them: it discloses data provenance (posted Amazon fee events joined to the seller's own orders and saved costs) and that fees include consumption tax actually charged (AU GST). This directly shapes how an agent should interpret the numbers.

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?

Three sentences, front-loaded with the purpose, then provenance, then usage triggers — well ordered with no redundancy. The provenance sentence is dense but justified; the 'such as AU GST' aside is slightly tangential but harmless.

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 no output schema, the description compensates by enumerating the returned metrics and the per-currency grouping, and it explains how the values are derived so the agent can trust and interpret them. Combined with 100% schema coverage on inputs, an agent has what it needs to call and interpret this tool.

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 all five parameters (date, days, end_date, start_date, marketplace_id) are already documented with usage rules in the schema. The description only says 'over a window' and 'grouped per currency', adding nothing about parameter syntax or precedence beyond what the schema provides. Baseline 3 applies.

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?

States a specific verb and resource ('profit and loss ... over a window') and enumerates the returned metrics (revenue, Amazon fees, COGS, net profit, margin, per currency). Scope ('for the connected Amazon seller account') implicitly distinguishes it from the per-SKU sibling, but it never names get_profit_by_sku, so the differentiation is left to inference.

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?

Gives clear trigger phrasing ('what did I actually make', 'was last month profitable', 'what are my fees'), which is strong positive guidance. However, it offers no exclusions or alternatives — it never says when to prefer get_profit_by_sku or another sibling — so an agent must infer the routing boundary.

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

All Amazon Seller MCP Server — AgentXray tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?