Amazon Product Research MCP Server
Provides tools to search Amazon products and retrieve detailed product information by ID.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Amazon Product Research MCP Server find wireless headphones under $100"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Amazon Product Research MCP Server
A Model Context Protocol (MCP) server that gives an AI assistant (e.g. Claude Code) tools to search and inspect Amazon product data, over stdio.
Features
search_products— search products by keyword.get_product— fetch full details for a single product by ID.Written in strict TypeScript, validated at runtime with Zod.
Unit tested with Vitest.
Linted with ESLint (
typescript-eslint) and formatted with Prettier.
Related MCP server: Amazon Product Intelligence Agent
Project structure
src/
├── index.ts # Entry point: wires the server to a stdio transport
├── server.ts # Registers MCP tools on the McpServer instance
├── types/
│ └── product.ts # Shared Product type
└── tools/
├── product-data.ts # In-memory product catalog (mock data)
├── search-products.ts # search_products tool: input schema + handler
└── get-product.ts # get_product tool: input schema + handler
tests/
├── search-products.test.ts
└── get-product.test.tsRequirements
Node.js 20+
npm
Getting started
npm install
npm run devThis starts the server over stdio using tsx, ready to be connected to by an
MCP client.
Connecting from Claude Code
claude mcp add amazon-research -- npx tsx /absolute/path/to/amazon-product-research-mcp/src/index.tsScripts
Command | Description |
| Run the server directly from TypeScript source. |
| Type-check and compile to |
| Run the compiled server from |
| Run the unit test suite (Vitest). |
| Lint the codebase with ESLint. |
| Format the codebase with Prettier. |
| Check formatting without writing changes. |
Tools reference
search_products
Search Amazon products by keyword (matches against the product name, case-insensitive).
Input
{ "query": "kindle" }Output
[
{
"id": "kindle-paperwhite",
"name": "Kindle Paperwhite",
"price": 169.99,
"currency": "USD",
"rating": 4.7,
"reviewCount": 12543,
"description": "A high-resolution e-reader designed for reading comfortably indoors and outdoors."
}
]get_product
Get full details for a single product by its ID.
Input
{ "product_id": "kindle-paperwhite" }Output: a single Product object, or null if no product matches.
Data
Product data currently lives in a small in-memory mock catalog
(src/tools/product-data.ts) rather than a live Amazon API — this keeps the
project self-contained and safe to run without any credentials. .env.example
documents the environment variables a real API integration would need.
License
MIT © Ludovic Delot Bravo
Available Tools
2 toolsget_productC
Get detailed information about an Amazon product.
Inputs: product_id (required).
search_productsB
Search Amazon products by keyword.
Inputs: query (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools are clearly distinct: search_products finds products by keyword, while get_product retrieves detailed information for a specific product. No overlap or ambiguity exists between them.
Both tools follow a consistent verb_noun pattern using snake_case (search_products, get_product), making the naming predictable and easily understandable.
With only 2 tools, the set feels thin for a product research server. While it covers the basic search-and-detail flow, it lacks breadth seen in more comprehensive servers, but it is not extreme enough to be a 1 or 2.
The tool surface provides the minimum necessary for product research—searching and retrieving details—but notably lacks capabilities like browsing categories, accessing reviews, or comparing products, which are expected in a full research context.
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