Amazon India Product Research MCP

generate_listing

Generate a full Amazon India product listing, including SEO title, benefit-led bullet points, product description, backend keywords, image direction, packaging advice, and compliance checklist.

Instructions

Generate an Amazon India listing: SEO title plus alternatives, five benefit-led bullet points, product description, backend search terms, keyword placement strategy, main / lifestyle / infographic / comparison image direction, packaging advice and a compliance checklist.

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": {
      "product_name": "<product_name>",
      "features": "<features>"
    },
    "name": "generate_listing"
  }
}

Input Schema

Name	Required	Default
`features`	Yes
`product_name`	Yes
`target_market`	No	India
`target_keywords`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Aug 23, 2026

TDQS

A3.7/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the burden. It discloses the output comprehensively (title, bullets, etc.) but does not mention the operation's side effects (e.g., no writes, but no explicit read-only claim). It does not mention any limitations, such as dependency on input quality.

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, dense sentence that covers the full scope without fluff. It is slightly long but front-loads the verb and resource, and each phrase contributes to the listing components.

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 the tool's complexity (4 params with 0% schema coverage) and the rich output schema, the description covers the output structure well, but does not specify input requirements (e.g., what constitutes a valid 'features' array) or how parameters influence the output. Still, the provided coverage is sufficient for an agent to call it correctly.

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 0%, but the description explains the overall purpose. It does not detail how each parameter maps to the output (e.g., target_keywords and target_market) beyond the general purpose. Given zero coverage, a baseline of 3 is appropriate, but it could be higher if it explicitly clarified how target_market affects the listing or how features are used.

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 uses a specific verb 'generate' and a clear resource 'an Amazon India listing', listing the exact components (SEO title, bullets, etc.). It clearly distinguishes this from other tools like research_product or analyze_competition.

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 implies usage for creating a full listing but does not specify when to use it versus alternatives, nor mention any prerequisites (e.g., has product research been done). It does not provide explicit when-to-use or when-not-to-use guidance.

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