commerce-ops-mcp
This server provides read-only tools to investigate why an e-commerce order is delayed and what to do about it.
search_orders: Find candidate orders to investigate, optionally filtered by status (
pending,processing,completed,cancelled,failed) or customer ID.get_order_context: Gather all operational facts for a specific order (order details, customer, payment, items/products, inventory, fulfillment) as evidence.
diagnose_order_delay: Apply deterministic rules to identify the most likely cause of a delay (payment issue, inventory shortage, fulfillment delay/failure, cancellation, or no issue) with supporting evidence.
recommend_resolution: Combine diagnosis and context to return recommended next actions for an operations specialist.
All tools are read-only; no write or action tools are available. Data is synthetic and no authentication is required.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@commerce-ops-mcp Why is order ORD-12345 delayed and what should I do?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Commerce Operations MCP
Status: domain/data foundation plus a working MCP server exposing four read-only investigation tools, served over both local stdio and remote Streamable HTTP (hosted on Render). No write/action tool yet — see "Current limitations".
Hosted MCP URL: https://e-commerce-mcp.onrender.com/mcp (Streamable
HTTP, stateless — see "Connecting to the hosted server" below)
Working name: commerce-ops-mcp (may change before final delivery).
An AI-native commerce operations investigator focused on delayed-order investigation, exposed through a remotely hosted MCP server.
Problem
An operations specialist needs to answer, for a given order: "why is this order delayed, and what should I do about it?" Answering that well often requires checking payment status, inventory availability, and fulfillment state — but the product is that one investigation workflow, not standalone tooling for payments, inventory, or fulfillment.
Confirmed scope (client-clarified): a single, focused end-to-end workflow — delayed-order investigation — is the right level of scope. Separate coverage of all four commerce domains (orders, payments, inventory, fulfillment) as independent workflows is explicitly out of scope. The project intentionally does not provide independent workflows for payments, inventory, fulfillment, or general order management; those systems are consulted only as supporting evidence when investigating a delayed order.
Related MCP server: Fulfillment Triage MCP
Current scope
This repository implements the backend foundation plus a working MCP server on top of it:
Domain types and status enums
A synthetic, internally consistent commerce dataset (static TypeScript modules)
A repository layer over that dataset
An
OrderContextServicethat composes operational facts (not diagnoses) for a given orderA
DelayDiagnosisServicethat applies deterministic rules to those facts to identify the likely delay causeA
ResolutionServicethat maps a diagnosis to recommended next actionsAn MCP server exposing
search_orders,get_order_context,diagnose_order_delay, andrecommend_resolutionas tools, over two transports sharing the same tool registration (src/mcp/create-server.ts):src/mcp/server.ts— stdio, for local MCP clients (Claude Desktop/Code, MCP Inspector)src/mcp/http.ts— Streamable HTTP (stateless), deployed remotely on Render for AI agents to connect to without any local setup
Tests covering all of the above
No write/action tool (create_resolution_ticket), LLM/agent integration,
database, or frontend exists yet. See CLAUDE.md for the explicit scope
boundary and docs/assumptions.md / docs/decisions.md for what's decided
vs. pending.
Architecture
Synthetic data (src/data/*.ts — static, typed TypeScript modules)
↓
Repositories (src/repositories/*.repository.ts)
↓
Domain services (order-context.service.ts, delay-diagnosis.service.ts,
resolution.service.ts)
↓
src/mcp/create-server.ts — search_orders, get_order_context,
diagnose_order_delay, recommend_resolution
↓ ↓
src/mcp/server.ts (stdio) src/mcp/http.ts (Streamable HTTP → Render)Repositories hide the underlying data representation; services and the MCP
tools depend only on repository method signatures, not on how the data is
stored. See docs/decisions.md for why static TypeScript modules were
chosen over PostgreSQL, why the MCP tool set stops at four read-only tools
for now, and why the remote transport runs in stateless mode.
Connecting to the hosted server
No local setup is required to try the deployed workflow — point any MCP client that supports Streamable HTTP at:
https://e-commerce-mcp.onrender.com/mcpFor example, with the MCP Inspector:
npx @modelcontextprotocol/inspector
# then connect with transport "Streamable HTTP" and the URL aboveThe server is stateless and unauthenticated (read-only synthetic data only —
see docs/decisions.md for that tradeoff, stated explicitly rather than
left implicit). The /mcp endpoint is rate-limited to 60 requests per IP
per minute (in-memory, per-instance — see docs/decisions.md) as a basic
abuse guard given the lack of authentication.
Running locally
npm install
npm run typecheck
npm test
# stdio transport (for Claude Desktop/Code, MCP Inspector over stdio)
npm run build
npm start
# Streamable HTTP transport (what's deployed to Render)
npm run build
npm run start:http # listens on $PORT, defaults to 3000Point any MCP client (e.g. npx @modelcontextprotocol/inspector node dist/server.js, or Claude Desktop/Code's MCP config) at npm start (from
this directory) to connect over stdio.
Testing
Tests use Vitest and live in tests/, mirroring
src/:
tests/repositories/— finder methods, including missing-record casestests/services/—OrderContextService(context composition, including theORD-1003inventory-shortage case),DelayDiagnosisService(all seven scenario orders map to their expected category), andResolutionServicetests/data/— cross-record invariant checks (foreign keys, order totals, one-record-per-order cardinality, required scenario orders) run against the real dataset and against deliberately broken copies of it — checks TypeScript's structural typing can't express on its own
Synthetic data
src/data/ contains 18 products, 15 customers, 50 orders, and their
corresponding payments/inventory/fulfillment records — all synthetic,
INR-priced, .test-domain emails, MockPay as the fake payment provider.
Each entity is a static, typed TypeScript module (e.g. src/data/orders.ts)
rather than a JSON file; the dataset was generated once and is now
finalized and hand-editable in place — see docs/decisions.md.
Seven orders (ORD-1001–ORD-1007) are deterministic, hand-crafted
scenarios representing known operational situations (payment failure,
inventory shortage, fulfillment delay, etc.) — see
docs/test-scenarios.md for the full table.
Current limitations
No write/action tool (
create_resolution_ticket) — read-only investigation, diagnosis, and recommendation only.No persistence beyond static TypeScript modules; no database.
No authentication/authorization on the hosted HTTP endpoint — deliberate and disclosed, not an oversight; see
docs/decisions.md. Acceptable here because the tool set is read-only and the data is synthetic; would need revisiting before any real data touched this server. Mitigated in part by a per-IP rate limit (60 req/min, in-memory) on/mcp, but that limits abuse per-instance only, not a substitute for real auth.No real external integrations of any kind.
Future stages
Not yet built, and out of scope for this submission: one controlled,
confirmation-gated write action (create_resolution_ticket), and
authentication on the hosted endpoint if this ever handled real data. See
CLAUDE.md and docs/decisions.md / docs/assumptions.md for what's
confirmed vs. still open.
Available Tools
4 toolsdiagnose_order_delayDiagnose order delayA
Apply deterministic rules to an order's context to identify the most likely cause of a delay (payment issue, inventory shortage, fulfillment delay/failure, cancelled, or no issue), with supporting evidence.
Inputs: orderId (required).
get_order_contextGet order contextA
Gather the operational facts for one order — order, customer, payment, items+product, inventory, fulfillment — as evidence for investigating why it may be delayed. Returns facts only, no diagnosis.
Inputs: orderId (required).
recommend_resolutionRecommend resolutionA
Diagnose an order's delay and return recommended next actions for an operations specialist to take.
Inputs: orderId (required).
search_ordersSearch ordersA
Find candidate orders to investigate, optionally filtered by status and/or customer id. Read-only lookup — not general order management.
Inputs: status, customerId.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 4 tools
Each tool serves a distinct stage in the investigation pipeline: search for candidates, gather context, diagnose cause, and recommend actions. Even though recommend_resolution includes diagnosis, the primary outputs are clearly different, so there is no ambiguity in selection.
All tool names follow a consistent verb_noun pattern in snake_case (search, get, diagnose, recommend), making the operation of each tool predictable and easy to understand.
Four tools is well-scoped for an order-delay investigation workflow, covering the essential stages without unnecessary bloat or omissions. The count fits comfortably within the ideal 3-15 range.
The tool set covers the full lifecycle of investigating an order delay: finding candidates, gathering contextual data, diagnosing the root cause, and recommending next steps. There are no obvious gaps for the stated purpose.
Maintenance
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