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amazon_charts

Retrieve ranked Amazon charts for Best Sellers, New Releases, or Most Wished For by department or subcategory. Use with browse node IDs for specific page results.

Instructions

Amazon product charts. Returns one page of a ranked Amazon chart (Best Sellers, New Releases, or Most Wished For) for a department or department subcategory on amazon.com. Discover valid department/node values with amazon-charts-categories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodeNoNumeric browse node id
pageNo1-based page number
chartYesChart type
departmentYesAmazon department slug

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.5

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that the tool returns 'one page' (implying pagination) and that it operates on amazon.com, which is useful. However, it doesn't disclose what the response format looks like, whether there are rate limits, or what happens with invalid department/node values. The pagination hint is a positive but the description could be more transparent about the output structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words. It front-loads the core purpose, then adds the scoping detail and the pointer to the sibling tool. Every sentence earns its place.

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

Completeness4/5

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

For a list/chart tool with 4 parameters and no output schema, the description covers the essential context: what it returns, the chart types, the scope, and how to discover valid parameter values. It doesn't describe the response format, but the absence of an output schema makes that a minor gap. The pointer to amazon-charts-categories is a strong completeness signal.

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 the schema already documents all four parameters. The description adds context by explaining that 'department' is a slug and 'node' is a numeric browse node id, and it points to amazon-charts-categories for discovering valid values. This is helpful but doesn't go far beyond what the schema already provides.

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 ('Returns'), a specific resource ('one page of a ranked Amazon chart'), and the exact chart types (Best Sellers, New Releases, Most Wished For) and scope (department or subcategory on amazon.com). It also distinguishes itself from the sibling amazon-charts-categories by pointing to it for discovering valid department/node values.

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?

The description clearly states what the tool returns and mentions the sibling tool for discovering valid department/node values, which gives the agent a path for finding required parameters. It doesn't explicitly state when NOT to use this tool or compare it to other chart tools, but the context is clear enough for an agent to select it appropriately.

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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