Amazon India Product Research MCP

analyze_purchase_signals

Evaluate Amazon.in keyword demand from 'X bought in past month' badges: counts listings with the badge, estimates units and revenue, flags strong performers, and gives a demand verdict.

Instructions

Aggregate Amazon India's 'X bought in past month' badges across a keyword: how many listings show one, total units, implied revenue, which listings clear a minimum monthly sales bar, and an overall demand verdict. The badge is Amazon's own published figure, making it the most reliable free sales signal available.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "keyword": "<keyword>"
    },
    "name": "analyze_purchase_signals"
  }
}

Input Schema

Name	Required	Default
`keyword`	Yes
`marketplace`	No	amazon.in
`max_listings`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Aug 23, 2026

TDQS

B3.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains what it returns (aggregated badge data, revenue, verdict) but does not mention potential side effects, data freshness, or limitations beyond the badge being a reliable signal. It doesn't contradict anything, but it doesn't fully disclose all behavioral aspects.

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, fairly dense sentence that front-loads the action and lists outputs. It is concise but slightly wordy (e.g., 'making it the most reliable free sales signal available' could be trimmed). Overall, it is well-structured and not verbose.

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?

Given no output schema, the description lists the expected outputs (count, units, revenue, list of qualifying listings, verdict) and explains the badge's significance. It does not specify the output format or handle edge cases, but for a tool with this complexity, it covers the essential outputs.

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

Parameters1/5

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

Schema coverage is 0% (no parameter descriptions). The description does not mention any of the parameters (keyword, marketplace, max_listings) or explain their meaning, defaults, or how they affect the output. This is a critical gap given the lack of schema descriptions.

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 clearly states the tool's purpose: aggregating Amazon India's 'X bought in past month' badges across a keyword, and lists the specific outputs (count, total units, revenue, etc.). It distinguishes itself from siblings like analyze_product_demand or research_keywords by focusing on this specific badge-based signal.

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

Usage Guidelines3/5

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

The description implicitly suggests it is for demand analysis via badges but does not explicitly mention when to use it over alternatives. It lacks direct comparison to sibling tools or conditions for when not to use it.

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