Connect AI Agents to Amazon Data via Glade API MCP

Glade API’s MCP (Model Context Protocol) endpoint exposes all 17 Amazon data operations as typed tools at /api/mcp. Any MCP-compatible AI agent, LLM, or autonomous workflow can call these tools to fetch live Amazon product, search, review, offer, and sales data without writing custom integration code.

What is MCP?

MCP (Model Context Protocol) is an open standard for exposing typed tools to AI agents. Clients send JSON-RPC requests describing which tool to call and with what parameters. Glade API executes the corresponding Amazon data operation and returns structured, schema-validated results. Every piece of marketplace content that flows through the MCP endpoint — product titles, descriptions, review bodies, seller names — is treated as opaque data, never as tool instructions. This design prevents AI agents from being manipulated by content embedded in Amazon listings or reviews.

Glade API’s MCP endpoint passes marketplace content as opaque data objects, never as instructions. This prevents AI agents from being manipulated by product descriptions or reviews.

Connecting to the MCP endpoint

Send JSON-RPC 2.0 requests to the following endpoint:

POST https://api.glade.dev/api/mcp

Glade API accepts your API key in any of these header formats, so you can use whichever convention your MCP client already sends:

Header	Example value
`API-KEY`	`glade_live_lookup_your_key`
`GLADE-API-KEY`	`glade_live_lookup_your_key`
`X-API-KEY`	`glade_live_lookup_your_key`
`Authorization`	`Bearer glade_live_lookup_your_key`

The endpoint is compatible with any MCP client, including Claude Desktop, Cursor, and custom agents built on the MCP SDK.

Claude Desktop
Cursor
Custom agent (Python MCP SDK)

Add the following to your claude_desktop_config.json under the mcpServers key:

{
  "mcpServers": {
    "glade": {
      "url": "https://api.glade.dev/api/mcp",
      "headers": {
        "API-KEY": "glade_live_lookup_your_key"
      }
    }
  }
}

Open Settings → MCP and add a new server entry:

{
  "name": "glade",
  "url": "https://api.glade.dev/api/mcp",
  "headers": {
    "API-KEY": "glade_live_lookup_your_key"
  }
}

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

async with streamablehttp_client(
    "https://api.glade.dev/api/mcp",
    headers={"API-KEY": "glade_live_lookup_your_key"},
) as (read, write, _):
    async with ClientSession(read, write) as session:
        await session.initialize()
        result = await session.call_tool(
            "get_AmazonProduct",
            {"asin": "B0D1XD1ZV3", "domain": "US"},
        )

Installing the Agent Skill

Glade API provides a downloadable amazon-data Skill file designed to help AI agents use the MCP endpoint effectively. The Skill documents safe API key setup, how to choose a marketplace, pagination strategies, cost awareness, data freshness considerations, and when to prefer REST, GraphQL, or MCP for a given task. Download the Skill file and add it to your agent’s context:

https://gladeapi.com/skills/amazon-data/SKILL.md

With the Skill loaded, your agent can make informed decisions about which tool to call, how many pages to fetch, and how to interpret result fields — without you needing to encode that logic manually in a system prompt.

Available tools

All 17 REST operations are available as MCP tools. Every tool uses the same parameter validation and response contract as the corresponding REST endpoint.

Tool name	What it fetches
`get_AmazonProduct`	Full product details by ASIN, GTIN, or URL
`get_AmazonProductVariants`	All variants (color, size, style, etc.) for a product
`get_AmazonProductReviews`	Paginated reviews with rating and keyword filtering
`get_AmazonProductOffers`	Third-party seller offers for an ASIN
`get_AmazonStockEstimates`	Current stock level and availability signals
`get_AmazonSalesEstimates`	Weekly, monthly, and annual unit sales estimates
`get_AmazonGtinFromAsin`	Barcode (UPC/EAN) lookup from an ASIN
`get_AmazonAsinFromGtin`	ASIN lookup from a barcode
`get_AmazonProductSearchResults`	Keyword search with category, price, and sort filters
`get_AmazonSearchAutocompleteResults`	Search suggestions for a partial query
`get_AmazonDeals`	Current deals and lightning deals by category
`get_AmazonBestSellers`	Best-seller rankings by category node
`get_AmazonBestSellerCategories`	Available best-seller category trees
`get_AmazonSeller`	Seller profile and active product listings
`get_AmazonAuthor`	Author profile and book listings
`get_AmazonProductCategory`	Category details and products in a category
`get_AmazonProductCategoryTaxonomy`	Full category tree for a marketplace

Example agent workflow

Here’s a complete example of an AI assistant helping a user find the best wireless earbuds under £50 in the UK marketplace. The agent chains four MCP tool calls to produce a personalized recommendation.

Search for matching products

The agent calls get_AmazonProductSearchResults with searchTerm="wireless earbuds", domain="UK", and maxPrice="50". The response returns a ranked list of products with titles, ASINs, prices, and ratings.

Select the top candidates

The agent filters the results to the three products with the highest rating values. Products with fewer than a threshold number of ratingsTotal (for example, fewer than 100 reviews) are deprioritized to avoid noise from lightly reviewed listings.

Fetch reviews for each candidate

The agent calls get_AmazonProductReviews for each of the three ASINs, filtering to rating=FIVE_STAR and onlyVerifiedReviews=true. This surfaces the strongest positive signals from confirmed buyers.

Summarize and present

The agent reads the body field from the top reviews for each product and synthesizes a short pros/cons summary for the user — grounded in real customer language, not marketing copy.

Each step consumes one unit. For cost-conscious agents, prefer GraphQL to fetch only the fields you need, reducing response size while keeping unit cost the same.

The same chaining pattern applies to many other research and shopping tasks: sourcing workflows, competitor analysis, catalog enrichment, and inventory monitoring. Because every tool returns structured, typed data, agents can reliably parse and act on results without fragile text extraction.

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Core Concepts

Guides

Connect AI Agents to Amazon Data via Glade API MCP

What is MCP?

Connecting to the MCP endpoint

Installing the Agent Skill

Available tools

Example agent workflow

​What is MCP?

​Connecting to the MCP endpoint

​Installing the Agent Skill

​Available tools

​Example agent workflow

What is MCP?

Connecting to the MCP endpoint

Installing the Agent Skill

Available tools

Example agent workflow