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smartscout_product_history

Retrieve historical price and sales rank changes for an Amazon product by ASIN to analyze trends over a chosen period.

Instructions

Get historical data for a product including price and sales rank changes

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinYesASIN of the product
daysNoNumber of days of history to retrieve

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what data is returned (price and sales rank changes) but omits critical details such as whether data is real-time or cached, rate limits, authentication requirements, or the granularity of history (daily, weekly). It does not disclose any side effects or data freshness, leaving significant gaps.

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 description is a single concise sentence that is front-loaded with the main action and resource. It is appropriately sized for a simple tool with two parameters and no complex behaviors. There is no wasted text, though it could be slightly more informative without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, no annotations, and relies on the description to convey its behavior. While the description states what data is returned, it lacks details on the format, granularity, and limitations of the historical data. For a tool that returns time-series data, this is a noticeable gap, though the basic information is present.

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%, meaning both parameters (asin and days) are already fully documented in the schema. The description does not add any meaning beyond what the schema provides – it does not explain the format of asin, the default behavior of days, or any constraints. When schema coverage is high, a baseline of 3 is appropriate even without additional parameter info.

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?

The description clearly states what the tool does: retrieves historical data (price and sales rank changes) for a product. It uses a specific verb ('Get') and resource ('historical data for a product'). However, it does not differentiate from siblings like smartscout_product_details or smartscout_product_search, which likely also return product data, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. There is no mention of when historical data is needed, conditions that make it preferable to product_details or product_search, or any exclusions. It only restates the purpose without usage context.

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