DEV Community

BuyWhere
BuyWhere

Posted on

Add live product search to an AI agent with one API call

#mcp #ai #api #agents

If you are building an AI agent that needs to answer "where should I buy this?", the hard part is rarely the model. The hard part is commerce plumbing.

Most agent stacks can already reason. What they cannot do out of the box is query a normalized catalog, compare products across merchants, and hand users off to a real listing without scraping HTML or stitching together merchant-specific integrations.

That is the gap BuyWhere is built to fill.

BuyWhere gives AI agents a product catalog layer they can call through one REST API or an MCP-compatible integration path. The canonical first request is GET /v1/products/search.

Why this matters for agent builders

Many agent workflows break down at the point where a user asks for something practical:

  • find the cheapest option in a category
  • compare the same product across merchants
  • recommend three choices under a budget
  • route the user to a real merchant page

Without a grounded product layer, teams usually choose between two bad options:

  1. scrape merchant websites and accept fragility
  2. build custom merchant integrations one by one

BuyWhere offers a cleaner path: one integration surface for product discovery and merchant handoff.

One request, real product candidates

The canonical quickstart call is simple:

curl -sS "https://api.buywhere.ai/v1/products/search?q=wireless+headphones&limit=5" \
  -H "Authorization: Bearer $BUYWHERE_API_KEY"
Enter fullscreen mode Exit fullscreen mode

That request returns structured product data your agent can actually use inside ranking, filtering, and recommendation logic.

Python example 

import os
import requests

response = requests.get(
    "https://api.buywhere.ai/v1/products/search",
    headers={"Authorization": f"Bearer {os.environ['BUYWHERE_API_KEY']}"},
    params={
        "q": "mechanical keyboard",
        "country_code": "SG",
        "max_price": 250,
        "limit": 5,
    },
    timeout=15,
)

response.raise_for_status()

for product in response.json().get("data", []):
    print(product["title"], product["price"], product["currency"], product["url"])
Enter fullscreen mode Exit fullscreen mode

That is enough to support a first useful behavior:

  • retrieve product candidates
  • rank them by your own criteria
  • return a recommendation with a merchant link

REST when you want control, MCP when you want velocity

Use the REST API when you want direct backend control, custom ranking, caching, or analytics.

Use the MCP path when your stack is already organized around tools and you want BuyWhere available inside an MCP-compatible client.

The protocol is not the main point. The main point is that your agent gets one product-search layer underneath it instead of brittle merchant-specific plumbing.

Start with one concrete workflow

If you want to evaluate BuyWhere, do not start by reading every doc. Start with one task:

  • find the cheapest product in a category
  • compare the same product across merchants
  • return three recommendations under a budget
  • route the user to the best merchant page

Then test it with a single call to GET /v1/products/search.

Quickstart:
https://buywhere.ai/quickstart?utm_source=devto&utm_medium=organic&utm_campaign=api_spotlight_distribution_2026_04_24

API keys:
https://buywhere.ai/api-keys?utm_source=devto&utm_medium=organic&utm_campaign=api_spotlight_distribution_2026_04_24

For agent builders, the value proposition is simple: one product-search layer, one integration surface, and a much shorter path from user intent to merchant handoff.

Top comments (0)