Canopy API MCP

The Canopy API MCP server is a TypeScript implementation of the Model Context Protocol that gives AI assistants and agents direct access to Amazon product data. It runs on Cloudflare Workers and exposes 11 tools covering product lookup, search, categories, sellers, and more.

Installation

Clone and deploy the MCP server from GitHub:

git clone https://github.com/canopy-api/canopy-api-mcp
cd canopy-api-mcp
npm install

Start a local development server:

npm run dev

The server will be available at http://localhost:8787/mcp.

Deploy to Cloudflare Workers:

npm run deploy

Connecting to Claude Desktop

Add the server to your Claude Desktop configuration file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "canopy": {
      "url": "http://localhost:8787/mcp",
      "headers": {
        "Authorization": "Bearer <YOUR_API_KEY>"
      }
    }
  }
}

Replace <YOUR_API_KEY> with your key from the Canopy API dashboard. After saving, restart Claude Desktop — the Canopy tools will appear in the tools panel.

Authentication

The server accepts your API key in any of these header formats:

CANOPY-API-KEY: your-api-key
API-KEY: your-api-key
X-API-KEY: your-api-key
Authorization: Bearer your-api-key

Available Tools

The server exposes 11 tools that AI assistants can invoke:

Tool	Description
`get_amazon_product`	Product details by ASIN, URL, or GTIN
`get_amazon_product_variants`	Product variants and options
`get_amazon_product_stock`	Stock level estimates
`get_amazon_product_sales`	Sales estimates (weekly/monthly/annual)
`get_amazon_product_reviews`	Top customer reviews
`search_amazon_products`	Search with filters and pagination
`get_amazon_autocomplete`	Search suggestions
`get_amazon_categories`	Root category taxonomy
`get_amazon_category`	Category details with subcategories
`get_amazon_seller`	Seller info and listings
`get_amazon_author`	Author info and books

Testing with MCP Inspector

You can test the server using the official MCP Inspector before connecting it to an AI client:

npx -y @modelcontextprotocol/inspector@latest

Connect the inspector to your running server URL (e.g. http://localhost:8787/mcp) and invoke tools interactively.