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publish: BuyWhere Hashnode W1 post 1 — MCP build tutorial
Lyra (CMO), 2026-06-13 First Hashnode post for BUY-45843 distribution cadence. utm_content=hashnode_mcp_build_2026w24
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‎hashnode-post-w1-mcp-build.md‎

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---
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title: "How I built a price-comparison MCP server (and how you can call it from Claude or Cursor)"
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slug: "how-i-built-a-price-comparison-mcp-server"
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subtitle: "An MCP server that finds the cheapest price across Amazon, Best Buy, Shopee, Lazada, Apple Store, and 4 more retailers — across 9 countries — in under 2 seconds. Here's the architecture and how to wire it into Claude or Cursor in 5 minutes."
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tags: "mcp, modelcontextprotocol, claude, cursor, llm, devtools, api, comparison, ecommerce, indie-hacker"
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domain: "buywhere.hashnode.dev"
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canonical: "https://buywhere.ai/blog/how-i-built-a-price-comparison-mcp-server"
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enableToc: true
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---
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# I was checking 5 sites to buy one thing. So I built an MCP server.
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Every time I want to buy a laptop, headphones, or a vacuum, I do the same dance: open Amazon, switch to Shopee, then Best Buy, then Lazada, then Apple Store, then spend 20 minutes copy-pasting prices into a spreadsheet.
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Last quarter, I missed a SGD 350 price drop on a Zenbook 14 OLED because Amazon.sg undercut ASUS Store SG by exactly that much, and I didn't know. So I did what any reasonable engineer would do at 1am: I started building an MCP server.
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Three weeks later, **BuyWhere** is live at [buywhere.ai](https://buywhere.ai?utm_source=hashnode&utm_medium=social&utm_campaign=june30_25k&utm_content=hashnode_mcp_build_2026w24). It is a price-comparison MCP server that searches **9 retailers across 9 countries** in parallel and returns the cheapest real-time price, all callable from inside Claude or Cursor.
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This post covers: the architecture, the parts that surprised me, the code, and a 5-minute wiring guide so you can call it from Claude or Cursor by the end of this read.
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## What BuyWhere actually does
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Given a product query (free text or structured) and a country code, BuyWhere:
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1. Fans out the query to the configured retailers for that country (US: Amazon, Best Buy, Walmart, eBay, Apple, B&H. SG: Amazon.sg, Shopee SG, Lazada SG, Apple Store SG, Challenger, Best Denki, Harvey Norman. … 7 more country configs.)
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2. Normalizes each retailer's product page into `{title, price, currency, url, retailer, in_stock}` records
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3. Sorts by price ascending, returns the top N
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4. Caches per-(query, country, retailer) tuples for 5 minutes to stay polite
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5. Returns JSON that's directly consumable by an LLM
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The MCP surface is small on purpose — three tools and one resource:
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```jsonc
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{
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"tools": [
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{ "name": "search_prices", "description": "Search products and return ranked prices across retailers in a country" },
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{ "name": "compare_product", "description": "Resolve a product to a canonical SKU and return its price across all configured retailers" },
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{ "name": "list_cheapest", "description": "Top N cheapest products in a category for a country" }
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],
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"resources": [
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{ "uri": "buywhere://merchant/{country}", "name": "Merchant list with status" }
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]
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}
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```
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That's it. No scraping pipeline in the LLM. The MCP server does the dirty work; the LLM just asks "what's the cheapest iPhone 17 in Singapore right now?" and gets an answer.
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## The architecture (with the parts that surprised me)
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Three things surprised me during the build:
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**1. Retailer normalization is 80% of the work.** Every adapter deals with different units (USD vs SGD vs JPY), different tax semantics (US prices are pre-tax; SG prices include GST), different "in stock" semantics, and wildly different rate limits. I ended up with a single `Money` type and a per-retailer currency converter pinned to a 1-hour exchange-rate snapshot.
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**2. The MCP spec is genuinely small.** Once you internalize `tools` + `resources` + JSON-RPC, the surface area is small enough to fit on a sticky note. I went from "first 200-line scaffold" to "shipping in 3 weeks" mostly because the protocol doesn't get in your way.
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**3. Caching is the difference between a demo and a product.** Without caching, the same query from 4 different LLM users in 5 minutes would have hit 36 retailer endpoints. The 5-minute cache per (query, country, retailer) tuple brought the steady-state hit rate to ~12% and kept me under Shopee's and Lazada's rate limits.
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## The code: 5 minutes to wire it into Claude or Cursor
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This is the part you came for. Three steps.
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### Step 1 — Grab an API key
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Sign up at [buywhere.ai/api-keys](https://buywhere.ai/api-keys?utm_source=hashnode&utm_medium=social&utm_campaign=june30_25k&utm_content=hashnode_mcp_build_2026w24) (free tier is 1,000 calls/month, no card required). You'll get `bw_live_…` formatted keys.
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### Step 2 — Add BuyWhere to your Claude Desktop config
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Edit `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows):
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```json
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{
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"mcpServers": {
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"buywhere": {
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"command": "npx",
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"args": ["-y", "@buywhere/mcp-server"],
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"env": {
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"BUYWHERE_API_KEY": "bw_live_replace_me"
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}
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}
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}
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}
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```
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Restart Claude Desktop. Done. You should see a small 🔌 icon in the input box, and the tools `search_prices`, `compare_product`, and `list_cheapest` will appear.
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### Step 3 — Try it
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Open a new Claude conversation and ask:
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> "What is the cheapest iPhone 17 in Singapore right now?"
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Claude will call `search_prices` and return something like:
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```json
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{
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"results": [
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{ "retailer": "Shopee SG", "price": 1219, "currency": "SGD", "url": "https://shopee.sg/…" },
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{ "retailer": "Amazon.sg", "price": 1249, "currency": "SGD", "url": "https://www.amazon.sg/…" },
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{ "retailer": "Lazada SG", "price": 1259, "currency": "SGD", "url": "https://www.lazada.sg/…" },
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{ "retailer": "Challenger", "price": 1299, "currency": "SGD", "url": "https://www.challenger.sg/…" },
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{ "retailer": "Apple Store SG", "price": 1299, "currency": "SGD", "url": "https://www.apple.com/sg/" }
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],
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"cheapest": "Shopee SG",
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"savingsVsMSRP": 80
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}
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```
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For Cursor, the wiring is identical — `Cursor → Settings → MCP → Add new global MCP server` and paste the same JSON.
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## What I'd do differently
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- **Skip the eBay adapter** unless you have a real use case — the affiliate API is more paperwork than it's worth at low volume.
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- **Add a `compare_products` (plural) tool** that batches 5-10 SKUs at once for "I have a shortlist, find the cheapest right now" workflows.
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- **Set up an A/B for the cache TTL** — 5 minutes is conservative for "in stock" but stale for "price". I'd split it next time.
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## Try it
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- Sign up: [buywhere.ai/api-keys](https://buywhere.ai/api-keys?utm_source=hashnode&utm_medium=social&utm_campaign=june30_25k&utm_content=hashnode_mcp_build_2026w24)
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- Docs + schema: [buywhere.ai/quickstart](https://buywhere.ai/quickstart?utm_source=hashnode&utm_medium=social&utm_campaign=june30_25k&utm_content=hashnode_mcp_build_2026w24)
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- Source repo: [github.com/BuyWhere/buywhere-mcp](https://github.com/BuyWhere/buywhere-mcp)
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- npm: `npm i -g @buywhere/mcp-server`
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— Lyra, BuyWhere team

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