npm install -g @buywhere/mcp-server

npx @buywhere/mcp-server

{
  "mcpServers": {
    "buywhere": {
      "command": "npx",
      "args": ["@buywhere/mcp-server"]
    }
  }
}

from buywhere import BuyWhereClient

client = BuyWhereClient()

# Search gaming laptops available in Singapore
results = client.search_products(
    query="gaming laptop",
    country="SG",
    limit=20
)

for product in results.products:
    print(f"{product.title}")
    print(f"  {product.price.amount} {product.price.currency}")
    print(f"  {product.merchant_name}")
    print()

def segment_by_tier(products):
    tiers = {
        "budget": [],      # S$1,000 - S$1,500
        "mid": [],         # S$1,500 - S$2,200
        "high": []         # S$2,200+
    }

    for p in products:
        if not p.price:
            continue
        sgd = p.price.amount

        if sgd < 1_500_00:   # $1,500 SGD in cents
            tiers["budget"].append(p)
        elif sgd < 2_200_00:
            tiers["mid"].append(p)
        else:
            tiers["high"].append(p)

    return tiers

tiers = segment_by_tier(results.products)
for tier_name, items in tiers.items():
    print(f"\n{tier_name.upper()} TIER ({len(items)} options):")
    for p in items[:3]:  # Top 3 per tier
        print(f"  {p.title[:60]}")
        print(f"  {p.price.amount/100:.2f} SGD")

import sqlite3
from datetime import datetime

def save_snapshot(products, db_path="gaming_laptops.db"):
    conn = sqlite3.connect(db_path)
    c = conn.cursor()

    c.execute("""
        CREATE TABLE IF NOT EXISTS price_history (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            product_id TEXT,
            title TEXT,
            price_cents INTEGER,
            currency TEXT,
            merchant TEXT,
            captured_at TEXT
        )
    """)

    now = datetime.utcnow().isoformat()
    for p in products:
        if not p.price:
            continue
        c.execute("""
            INSERT INTO price_history 
            (product_id, title, price_cents, currency, merchant, captured_at)
            VALUES (?, ?, ?, ?, ?, ?)
        """, (p.id, p.title, p.price.amount, p.price.currency,
              p.merchant_name, now))

    conn.commit()
    conn.close()

# Run daily via cron or CI pipeline
results = client.search_products(query="gaming laptop", country="SG", limit=50)
save_snapshot(results.products)
print(f"Snapshot saved: {len(results.products)} products")

def find_price_drops(db_path="gaming_laptops.db", drop_threshold_pct=5):
    conn = sqlite3.connect(db_path)
    c = conn.cursor()

    c.execute("""
        SELECT 
            ph1.product_id,
            ph1.title,
            ph1.merchant,
            ph1.captured_at as first_seen,
            ph2.captured_at as last_seen,
            ph1.price_cents as old_price,
            ph2.price_cents as new_price,
            ROUND((ph1.price_cents - ph2.price_cents) * 100.0 / ph1.price_cents, 1) as drop_pct
        FROM price_history ph1
        JOIN (
            SELECT product_id, merchant, MIN(captured_at) as min_dt, MAX(captured_at) as max_dt
            FROM price_history
            GROUP BY product_id, merchant
        ) latest ON ph1.product_id = latest.product_id 
            AND ph1.merchant = latest.merchant
            AND ph1.captured_at = latest.max_dt
        JOIN price_history ph2 ON ph1.product_id = ph2.product_id
            AND ph1.merchant = ph2.merchant
            AND ph2.captured_at = latest.min_dt
        WHERE ph1.price_cents < ph2.price_cents
            AND (ph2.price_cents - ph1.price_cents) * 100.0 / ph2.price_cents >= ?
        ORDER BY drop_pct DESC
    """, (drop_threshold_pct,))

    return c.fetchall()

drops = find_price_drops(drop_threshold_pct=5)
print(f"\n{'='*60}")
print(f"PRICE DROPS > 5%:")
print(f"{'='*60}")
for row in drops[:10]:
    pid, title, merchant, first, last, old, new, pct = row
    print(f"\n  {title[:60]}")
    print(f"  {merchant}")
    print(f"  S${old/100:.2f} → S${new/100:.2f} ({pct}% drop)")

You have access to the BuyWhere MCP server. When a user asks about
product prices or recommendations:

1. Use search_products to find relevant products in their country
2. Filter by the user's stated budget and use case
3. Show the top 3-5 options with prices from live merchant data
4. Include the affiliate link so they can buy through your recommendation

Always verify prices are current by checking the returned price field.
Never recommend a product without checking live availability.