Easyparser — Amazon Product & Seller Data

get_sales_history

Read-only

Retrieve a product's historical performance over up to 12 months: weekly aggregated trends for estimated views/traffic, sales, price, and Best Sellers Rank. This is Easyparser's 'time machine' — the deepest competitive-intelligence tool in this server.

Use this tool for trend forecasting, seasonality analysis, conversion-rate estimation, and investment/sourcing due diligence. Do NOT use it for a simple current price check — get_product_detail is 5x cheaper for that.

IMPORTANT cost rule: the base cost is 5 credits, and each month of history adds 1 credit (3 months = 8 credits, 12 months = 17 credits). Always ask the user how far back they need, or default to 3 months for a quick trend read. The history array in the response is aggregated by WEEK, not by day.

Input Schema

Name	Required	Description	Default
`asin`	Yes	Amazon Standard Identification Number — a 10-character alphanumeric product identifier (e.g. B0CJB6V2L5). Found in the product URL after /dp/ or /gp/product/.
`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
`history_range`	No	Depth of historical data in months. '0' returns only the current snapshot (5 credits). '3', '6', '9', '12' add weekly history at +1 credit per month. Default '0'.	0

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

A4.8/5.0

Behavior5/5

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

Despite readOnlyHint/destructiveHint annotations already marking this as a safe read, the description goes far beyond them: it discloses the credit cost model (5 base + 1 per month with worked examples), the weekly aggregation granularity of the response, and an instruction to ask the user or default to 3 months. These are material behavioral traits not derivable from annotations or schema.

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-organized with a purpose sentence, usage section, and a clearly flagged 'IMPORTANT cost rule.' Front-loaded with what it does. Slightly repetitive — the weekly aggregation point appears twice and the credit formula is stated in two places — but the redundancy is deliberate emphasis, not 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 paid, read-only, time-series retrieval tool with an output schema and full param coverage, the description covers everything an agent needs: scope, cost, granularity, when to prefer a sibling, and default behavior. Nothing required 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 adds value beyond it by giving the cost implication of history_range (worked credit examples) and the default guidance ('default to 3 months for a quick trend read'). It reinforces that the history array is weekly, not daily. Minor deduction because enum semantics themselves are already fully documented in 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?

The description states a specific verb and resource ('Retrieve a product's historical performance over up to 12 months: weekly aggregated trends for views, sales, price, and BSR') and clearly distinguishes itself from siblings by naming get_product_detail as the cheaper alternative. An agent can tell exactly what this returns without opening the 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?

It provides explicit when-to-use cases (trend forecasting, seasonality, conversion-rate estimation, due diligence) and an explicit when-not-to-use rule ('Do NOT use it for a simple current price check — get_product_detail is 5x cheaper'), including a cost comparison to guide the choice. This is textbook routing guidance.

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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