Amazon
20 countriesSearch, product
The world's ecommerce.
One MCP.
200 free credits. No card, no subscription.
set up https://mcp.datawebot.com/setup.mdOr connect it yourself
datawebot is an ecommerce MCP server and API that lets AI agents search products in online shops worldwide, compare every seller's price for a product, and extract Amazon, Walmart and brand-store data as structured records.
Real results
Find running shoes under $100 that I can buy in the US. Show them as cards with price and shop.
search_products({"query":"running shoes under $100","country":"US"})
I need a 4 person camping tent from a UK shop. Show a few options with prices.
search_products({"query":"4 person camping tent","country":"GB"})
Search Japanese shops for an electric toothbrush, the brush itself and not replacement heads (電動歯ブラシ 本体), and show prices in yen.
search_products({"query":"電動歯ブラシ 本体","country":"JP"})
Real result · retrieved 23 Oct 2026 · See all prompts
Works where you build
Same tools, two ways in. No new SDK to learn.
You I need a 4 person camping tent from a UK shop. Show a few options with prices.
Agent search_products {"query":"4 person camping tent","country":"GB"}
→ Eurohike Sendero 4 Family Tent · £100.00 · Amazon.co.uk
→ Pro Action 4 Person 1 Room Tunnel Camping Tent · £80.00 · Argos
→ Halfords 4 Person Vis A Vis Tent XL · £50.00 · Halfords
→ Decathlon 4 Man Tent · £119.99 · Decathlon UK
(retrieved 23 Oct 2026)Connect once with the setup prompt above. Your agent calls the tools when your request needs them.
# pip install mcp
import asyncio, os
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
async def main():
headers = {"Authorization": f"Bearer {os.environ['DATAWEBOT_API_KEY']}"}
async with streamablehttp_client("https://mcp.datawebot.com/mcp", headers=headers) as (read, write, _):
async with ClientSession(read, write) as session:
await session.initialize()
result = await session.call_tool("search_products", {"query":"4 person camping tent","country":"GB"})
print(result.content[0].text)
asyncio.run(main())Create a key on the dashboard. LangChain, the OpenAI Agents SDK, the Vercel AI SDK and n8n work the same way: point their MCP client at the server with your key.
curl https://api.datawebot.com/v1/jobs \
-H "Authorization: Bearer $DATAWEBOT_API_KEY" \
-H "Idempotency-Key: amazon-coffee-0001" \
-H "Content-Type: application/json" \
-d '{"blueprint_id":"amazon.search_items","url":"https://www.amazon.com/s?k=coffee","parameters":{"max_pages":1}}'
# Then poll GET /v1/jobs/{job_id} until it finishes,
# and read GET /v1/jobs/{job_id}/results, following next_cursor.One Job is one URL. See the API reference for statuses, errors and limits.
Endpoints
Amazon in 20 countries, eBay in 17 and Zalando in 17, plus brands like Reebok, Steve Madden and Skechers in their country stores. Every search and product answer comes back in the same structured product record (title, price and currency, availability, link), so your agent never parses a page.
Search result pages for your keyword on Amazon, eBay, Walmart and Zalando; brand stores return up to 10 matching products.
One product page in full, with options, prices and availability.
A brand store's products with variants and prices, up to 250 a Job, ready for CSV. 5 credits a page; failed Jobs are free.
Search, product
Search, product
Search, product
Search, product, catalogue
Search, product, catalogue
Search, product, catalogue
Search, product, catalogue
Search, product, catalogue
Search, product, catalogue
Search, product, catalogue
Search, product, catalogue
Search, product, catalogue
Type a store name or domain, e.g. amazon.de.
How it works
Connect your coding agent, approve access, then ask for the product data you need.
Use a coding agent with a terminal and remote MCP support. The setup instructions guide the connection.
Check the sign-in code and approve your agent. Sign-up is free and open to anyone; no API keys pasted into chat.
Ask for products across online shops, compare seller offers with direct links, and keep the retrieval time with each result.
Search results are best-effort snapshots, not verified live prices. Amazon in 20 countries, Walmart and brand stores run as extraction Jobs, open to every account with 200 free trial credits. See every supported store.
Search products by keyword on one shop or across shops, in any country and its language: title, price, shop, rating and a product_id for each.
Read one product's details and, for a product sold by several shops, each seller's price, total with shipping, stock and a direct link.
Run extraction Jobs on search, product and catalogue pages from Amazon, Walmart and brand stores, with every store listed in the directory.
FAQ
datawebot is an ecommerce MCP server and API that lets AI agents search products in online shops worldwide, compare every seller's price for a product, and extract Amazon, Walmart and brand-store data as structured records.
Yes. Every account gets 200 free credits, enough for 40 searches or 200 product reads. No card is needed.
Claude Code, Codex, Cursor, opencode, ChatGPT, Claude.ai and any MCP client. Coding agents take the setup prompt; chat apps add https://mcp.datawebot.com/mcp as a connector. From your own code, use any MCP client with an API key.
Online shops worldwide, through product search in the country you choose. Coverage differs by country and product.
Amazon in 20 countries, Walmart and brand stores: more than 28,000 online stores in all. Every one is listed in the store directory.
Not in your agent: sign in by email, free and open to anyone. For your own code, create a key on the dashboard.
Every account starts with 200 free credits. A search costs 5 credits and a product read 1, with every seller's price. Failed requests are free; a page that loads but has no results is still charged. Then credit packs from $10, with no subscription.
No. Each call is a dated snapshot; your agent or app keeps the history.
Yes, but kept apart from the organic results. Listings the shop or search marks as sponsored come back in a separate sponsored list, each flagged as sponsored, and agents are told to label them. A sponsored shop offer is flagged and listed last. When your query includes a model code, such as WH-1000XM5, results for other models are left out; if nothing matches, all results are shown and the summary says so.
No. The logos illustrate ecommerce breadth, not partnerships or live integrations. Product search returns best-effort listings from online shops. Extraction covers the sites in the endpoint catalogue.