Easyparser — Amazon Product & Seller Data

get_seller_feedback

Read-only

Read individual buyer feedback entries for an Amazon seller: star ratings, comment text, and dates — filterable by rating range and time period. This is the reputation deep-dive tool.

Use this tool when the user wants to read actual buyer comments about a seller, analyze complaint patterns, or audit service quality. For aggregate reputation scores (30/90/365-day percentages) without individual comments, get_seller_profile is sufficient and cheaper in context.

Each page costs 1 credit. To find dissatisfied customers, set max_rating to 3 instead of paging through everything.

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
`max_page`	No	Ending page number. Each page costs 1 credit; max 5 pages per request. Default 1.
`min_page`	No	Starting page number. Default 1.
`seller_id`	Yes	Amazon's unique seller identifier (e.g. A1MCYUGJD2ILFU). Found in seller profile URLs as the 'seller=' or 'me=' parameter.
`max_rating`	No	Maximum star rating to include (1-5). Set to 3 to focus on negative feedback. Default 5.
`min_rating`	No	Minimum star rating to include (1-5). Default 1.
`history_range`	No	Time period filter: '1' or '3' or '12' for months, 'all' for everything. Default 'all'.	all

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 cover the safety profile (readOnlyHint, destructiveHint=false, openWorldHint), so the bar is lower; the description still adds meaningful non-annotation context: 1 credit per page, a 5-page cap, and the max_rating=3 trick to surface negative feedback without paging. It stops short of describing pagination semantics or auth requirements, hence not a 5.

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?

Well front-loaded: purpose first, then when-to-use, then cost/practical tip. Each paragraph is short and earns its place, though 'This is the reputation deep-dive tool' is mildly redundant marketing 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?

With an output schema present, the description needn't explain return values, and it still names the returned fields. Annotations cover safety, all 7 parameters are documented, cost is disclosed, and the sibling routing is explicit — nothing an agent needs to invoke it correctly is missing.

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 baseline is 3, but the description goes beyond the schema by explaining the practical intent of max_rating ('set max_rating to 3 instead of paging through everything') and tying rating/time filters to a goal. The other parameters (domain, page bounds, history_range) get no added meaning beyond the schema.

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 specific verb (Read) and resource (individual buyer feedback entries for an Amazon seller), enumerates the returned fields (star ratings, comment text, dates), and explicitly contrasts itself with the get_seller_profile sibling. An agent can distinguish it from get_seller_profile and get_seller_products without opening a 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 conditions (reading actual buyer comments, analyzing complaint patterns, auditing service quality) and names the alternative (get_seller_profile) with the exact condition that selects it (aggregate 30/90/365-day scores without comments) plus a cost rationale.

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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