Skip to main content
Glama
Utkarsh-Sinha0

Razor Dvara MCP Server

Razor Dvara

v0.1.0 · updated 2026-08-22 · gated agentic checkout for COD commerce

"I am trying to setup payment methods where I want to charge extra amount for COD Orders." — merchant on Shopify Community, Jan 1 2025 (thread, zero replies)

COD is still ~half of Indian e-commerce orders. It also drives most of the returns: Tier-2/3 markets see 58–64% COD but 76–83% of RTO volume (bepragma.ai, Jun 2026). Every failed delivery costs ₹180–240 in logistics alone.

Razor Dvara lets merchants set COD rules once. AI buyers get structured answers. The audit trail proves what happened.

What this does

Tool

What it answers

cod_fee

Should COD be allowed? What fee applies? Which rule fired?

partial_cod

How much should the buyer prepay before delivery?

offer_orchestrator

Which existing dashboard offer should attach to this order?

rto_risk

How risky is this delivery? What drives the score?

serviceability_check

Can we deliver here? Is the backend healthy?

Every response includes reason_code and rule_id. Every decision appends to an audit trail. If the backend dies, the edge cache serves stale data with a degraded flag and completes the order prepaid-only.

Related MCP server: Mercora

Architecture

AI buyer / checkout agent
         |
         | MCP (streamable HTTP)
         v
+-------------------------------------------+
| Razor Dvara MCP Server                    |
| 5 tools · zero overlap w/ official 45    |
+-------------------+-----------------------+
                    |
                    v
+-------------------------------------------+
| Policy Engine (src/gates/)                |
| merchant caps · PIN rules → reason + ID  |
+-------------------+-----------------------+
                    |
                    v
+-------------------------------------------+
| Audit Trail (Cloudflare D1)              |
| sequence numbers · UTC · append-only    |
+-------------------+-----------------------+
                    |
                    v
+-------------------------------------------+
| SWR Edge Cache                           |
| 8s origin timeout → stale fallback       |
| degraded flag → fail-closed COD          |
+-------------------------------------------+

Shopify side: Payment Customization Function
  keyed on dvara.cod.eligible metafield
  hides COD when any cart line ineligible

Full architecture with protocol lineage: docs/architecture.md

Honest constraints

  • Offers are dashboard-create-only. We attach existing offer IDs; we don't mint them.

  • RTO/COD Intelligence is dashboard-only with no public API. Our scorer uses calibrated weights from published industry rates — it's not ML.

  • Serviceability is an inbound contract we implement ourselves. Magic Checkout's documented 10-second hard kill is the SLA we guarantee.

  • Test mode only (rzp_test_ keys). No real money moves.

NOT building

  • Chat UI — this is infrastructure between agents and money actions, not a conversational product.

  • Real ML for RTO scoring — calibrated weights without delivery-outcome data would produce misleading precision.

  • Offer creation via API — Razorpay doesn't expose it; pretending otherwise would be dishonest.

  • Production deployment — buildathon prototype on test keys.

Evidence

Every claim maps to a checkable artifact: docs/EVIDENCE.md

Tests

pnpm install
pnpm test

36 tests across 7 files covering rule evaluation, risk scoring, serviceability, cache degradation, Shopify function behavior, and end-to-end pipeline determinism.

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    F
    maintenance
    Enables intelligent ecommerce tools for agents and applications, including product catalog access, product addition, and shopping policies.
    1
    Apache 2.0
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables AI shopping agents to search products, check stock, apply promotions, manage cart sessions, and create cryptographically signed checkout sessions on e-commerce storefronts, while giving merchants analytics into agent intent and catalog demand gaps.
    MIT