Easyparser — Amazon Product & Seller Data

lookup_product

Read-only

Convert an external product identifier (UPC, EAN, GTIN, ISBN, JAN, MINSAN) into the corresponding Amazon ASIN, with an essential product snapshot. This is the bridge between manufacturer/retail barcodes and the Amazon ecosystem.

Use this tool when the user has a barcode or standard product code and wants to find the matching Amazon listing. Typical workflows: catalog matching, inventory synchronization, converting supplier data into Amazon ASINs. Also accepts ASIN as input type if you need to validate one.

You MUST set identifier_type to match the code you have — guessing the wrong type returns no results. UPC is 12 digits, EAN/GTIN is 13, ISBN is 10 or 13 (books). If the user gives a keyword instead of a code, use search_products instead. Costs 1 credit.

Input Schema

Name	Required	Description	Default
`domain`	No	Amazon marketplace domain extension. Determines the regional Amazon site the data is fetched from. Use the domain that matches the user's market — prices, availability and rankings differ across marketplaces.	.com
`identifier`	Yes	The product code value (e.g. '724382975021' for a UPC). Digits only for barcode types.
`identifier_type`	Yes	The type of code provided. UPC: 12-digit US barcode. EAN/GTIN: 13-digit international barcode. ISBN: book identifier. JAN: Japanese barcode. MINSAN: Italian pharma code. ASIN: Amazon's own identifier.

Output Schema

Name	Required	Description
`data`	No	Parsed Amazon data for the operation.
`success`	Yes	True when the request completed.
`operation`	Yes	Easyparser operation that ran, e.g. DETAIL or OFFER.
`request_id`	No	Easyparser request ID, useful for support and error logs.
`credits_used`	No	Credits charged for this request.
`credits_remaining`	No	Credits left on the account after this request.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 11, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "credits_remaining": {
+      "description": "Credits left on the account after this request.",
+      "type": "number"
+    },
+    "credits_used": {
+      "description": "Credits charged for this request.",
+      "type": "number"
+    },
+    "data": {
+      "description": "Parsed Amazon data for the operation."
+    },
+    "operation": {
+      "description": "Easyparser operation that ran, e.g. DETAIL or OFFER.",
+      "type": "string"
+    },
+    "request_id": {
+      "description": "Easyparser request ID, useful for support and error logs.",
+      "type": "string"
+    },
+    "success": {
+      "description": "True when the request completed.",
+      "type": "boolean"
+    }
+  },
+  "required": [
+    "success",
+    "operation"
+  ],
+  "type": "object"
+}

First observedSep 6, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare this a safe, open-world read, so the description's job is to add context beyond that — and it does, disclosing the 1-credit cost and the failure mode ('guessing the wrong type returns no results'). It does not discuss rate limits or latency, and return shape is covered by the output schema, so it stops just short of full disclosure.

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?

The purpose sentence is front-loaded and each subsequent block (workflow, precondition, alternative, cost) earns its place. It is slightly long and would read faster as a tighter bulleted block, but there is no redundant filler.

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?

For a 3-parameter read tool with full schema coverage, an output schema, and annotations covering the safety profile, everything an agent needs is present: what it does, when to use it, the sibling to use instead, the type-matching requirement, and the credit cost.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3 and the enum descriptions already define each type. The description still adds decision-relevant semantics above the schema: concrete digit-length heuristics (UPC 12, EAN/GTIN 13, ISBN 10 or 13) and the behavioural consequence of mismatching the type.

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?

States a precise verb+resource transformation ('Convert an external product identifier ... into the corresponding Amazon ASIN') and enumerates the accepted code families. It also explicitly contrasts itself with the sibling search_products, so an agent can route correctly without opening either schema.

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?

Gives explicit when-to-use ('user has a barcode or standard product code and wants to find the matching Amazon listing'), sample workflows, and a clear when-not/alternative rule ('If the user gives a keyword instead of a code, use search_products instead'). It also flags the hard precondition that identifier_type must match the code.

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

All Easyparser — Amazon Product & Seller Data tools Inspect

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