DealsPing MCP Server

India's AI-powered deals discovery platform. Find the best deals on Amazon, Flipkart and more Indian stores via Claude and other AI platforms.

MCP Server URL

https://dealsping-ai.akhilbabumfwa.workers.dev/mcp

Transport: Streamable HTTP (JSON-RPC 2.0 over POST, optional SSE stream over GET).

Available Tools

Usage

Add to Claude: Settings → Connectors → Add custom connector → https://dealsping-ai.akhilbabumfwa.workers.dev/mcp

About DealsPing

DealsPing (dealsping.in) aggregates the best deals from Amazon, Flipkart, Myntra, Ajio and more Indian e-commerce stores.

As an Amazon Associate, DealsPing earns from qualifying purchases.

Architecture

Cloudflare Workers + D1 backend that mirrors DealsPing's Firebase deal data into a fast, AI-queryable API, MCP server, and admin dashboard. Completely separate from dealsping-next — read-only against Firebase, never writes back.

• sync.js — pulls deals + monitored_deals from Firestore REST API (no SDK), maps to D1 schema, upserts, marks stale deals inactive.
• engine.js — deal score (0-100) calculation, freshness decay, AI list rebuilding, 4-day category rotation.
• api.js — public REST API (/api/*), rate-limited, CORS-open, 5-minute cache headers.
• mcp.js — MCP server at /mcp (Streamable HTTP: JSON-RPC 2.0 handshake, tools/list, tools/call).
• admin.js — protected dashboard API (/admin/*), gated by X-Admin-Secret header.
• index.js — router + cron handler (sync every 6h, rebuild lists every 2d, rotate categories every 4d).

Setup

npm install

# 1. Create the D1 database
npx wrangler d1 create dealsping-ai-db
# → copy the returned database_id into wrangler.toml

# 2. Apply schema (local + remote)
npx wrangler d1 execute dealsping-ai-db --file=schema.sql
npx wrangler d1 execute dealsping-ai-db --file=schema.sql --remote

# 3. Set secrets
npx wrangler secret put FIREBASE_API_KEY   # your Firebase Web API key
npx wrangler secret put SYNC_SECRET        # random string
npx wrangler secret put ADMIN_SECRET       # random string

# 4. Deploy
npx wrangler deploy

Endpoints

AI Lists

best_deals, latest_deals, trending_deals, featured_deals, under_500, under_1000, under_2000, under_5000, electronics, fashion, home_kitchen, mobiles, with_coupon, with_bank_offer, amazon_deals, flipkart_deals.

Deal Score Formula (0-100)

discount_score   = (discount_pct / 100) * 25
freshness_score  = freshness (0-1, decays over 7 days) * 20
price_score      = tiered by current_price, max 15
trending_score   = 15 if trending else 0
featured_score   = 10 if featured else 0
coupon_bonus     = 10 if coupon_code else 0
bank_offer_bonus = 5 if bank_offer else 0

Notes / Limitations

• Rate limiting (60 req/min/IP) is best-effort in-memory per Worker isolate — not strictly distributed. Sufficient to blunt casual abuse; for hard guarantees, migrate to Durable Objects or a KV-backed counter.
• Affiliate URLs are copied verbatim from Firebase — never modified.
• Sync never touches Firebase; it's read-only via the Firestore REST API with an API key.
• firestore.rules on the source project already have allow read: if true on deals/monitored_deals, so the API key alone is sufficient (no service account needed for reads).
• The ASIN catalog (asin_catalog table, powering check_and_link/search_catalog) intentionally stores no price/image/availability data — see check_and_link's tool description for why.

License

MIT

Contact

akhilbabumfwa@gmail.com