Commerce Incident Investigator

MCP server for the DiligenceAI take-home assignment. It handles one specific commerce incident: a customer's payment was captured, but no order was ever created for it.

The MCP server is the actual product here, not a wrapper around one. An AI agent calls it to investigate the incident, pull together evidence, and raise a human-review escalation. It doesn't take any corrective action itself — no recreating orders, no touching payments. That part stays with a person on the ops team.

Why this incident, specifically

Payment-order mismatches are one of the more common things an ops team gets pulled into: a webhook fails, a race condition hits between capture and order creation, a retry gets mishandled somewhere upstream. It's also narrow enough to build and verify properly in the time given, rather than sprawling into a general-purpose "investigate anything" tool that ends up shallow everywhere.

How it works

1. Customer says "I paid but never got a confirmation."
2. An agent calls investigate_payment_incident with the checkout_reference from their payment.
3. The server looks up the payment and the order — read-only. Is the payment actually captured? Has enough time passed that this isn't just normal async delay? Does a matching order exist at all?
4. If all three point to a real incident, it writes an incident_escalations row: evidence, a confidence level, a recommended next step. That's the review item a human picks up.
5. list_open_escalations gives the ops team a queue. get_escalation_status checks in on one specific case.

The schema decisions that mattered

Full schema is in migrations/001_init.sql, but two things are worth calling out because they went through a few rounds of revision before landing:

The correlation key. Early on I was matching payments to orders through customer identity, which doesn't actually work — a customer can have several payments and orders, so there's no way to say this payment should map to that order. Fixed it with a checkout_reference: generated once at checkout, stamped on both the payment and the order it produces. Now the investigation has something deterministic to match against instead of guessing.

Duplicate escalations under concurrent investigations. If the same incident gets investigated twice — a retry job overlapping with a manual recheck, say — naive "check if it exists, then insert" logic can still race and create two escalations for one incident. I added a partial unique index on incident_escalations (checkout_reference) WHERE status IN ('open', 'acknowledged'), paired with INSERT ... ON CONFLICT ... DO UPDATE. That pushes the guarantee down into Postgres itself instead of trusting application code to get the timing right. Verified this isn't just theoretical — there's a test that fires 10 concurrent investigations at the same incident and checks exactly one row exists afterward.

The 5-minute buffer between "payment captured" and "treat missing order as an incident" is a documented assumption for this demo, not a real business policy — it exists so normal async order-creation delay doesn't get flagged as a false incident. It's the BUFFER_MINUTES constant in src/investigate.ts if it needs tuning.

What I left out, and why

• Inventory and fulfillment data. I went back and forth on this with the client during scoping. For this specific trigger (payment captured, order missing) there wasn't a concrete piece of inventory evidence that would change the classification without modeling a separate reservation step this workflow doesn't have. Rather than bolt it on for the sake of looking thorough, I cut it.
• Auth, a frontend, a real commerce backend. Explicitly out of scope per the assignment.
• Any automated fix. The server investigates and escalates. It never recreates an order or touches a payment. That's a deliberate line, not a missing feature.

Project layout

migrations/001_init.sql     schema — tables + the partial unique index
src/db.ts                   Postgres pool
src/migrate.ts               migration runner
src/seed.ts                  synthetic data covering 4 scenarios
src/investigate.ts           the actual investigation logic, kept separate
                              from the MCP/transport layer so it's testable
                              on its own
src/server.ts                MCP server, 3 tools, Streamable HTTP
src/smoke-test.ts            reproducible hosted-protocol check — see below
tests/investigate.test.ts    integration tests against a real Postgres DB

Running it locally

npm install
cp .env.example .env    # add your DATABASE_URL
npm run migrate
npm run seed
npm run dev              # server on :3000

GET /healthz for a basic check. The actual MCP endpoint is POST /mcp (Streamable HTTP — it's session-based, so a client needs to call initialize before it can call any tool).

Tests

npm test

Nine tests, all against a real database rather than mocks: the four seed scenarios, re-investigating an incident updates the existing escalation instead of duplicating it, an escalation auto-resolves once a delayed order shows up, and the concurrency test mentioned above.

One thing I caught while running these against Neon: the "within buffer window" scenario is inherently time-relative (it depends on how long ago the payment was seeded), so if you run seed and then run the tests several minutes later, that scenario can quietly become stale and the test fails — correctly, because the underlying behavior is actually right, the test data just aged out of the window it was meant to represent. Fixed by having the test suite seed that specific case fresh, immediately before asserting on it, instead of depending on npm run seed having been run recently.

Hosted-protocol smoke check

The test suite above runs the investigation logic directly against Postgres. It doesn't prove the deployed MCP endpoint itself actually speaks the protocol correctly over the network. This script does — it initializes a real MCP session against a live URL, lists the tools, calls investigate_payment_incident, and writes the raw request/response pairs to smoke-test-output.json so the result is inspectable and reproducible, not just something I ran once and pasted into a chat.

MCP_URL="https://diligence-mcp.onrender.com/mcp" npm run smoke:hosted

Defaults to http://localhost:3000/mcp if MCP_URL isn't set. Exits non-zero if the deployed server doesn't respond as expected, so it can also be used as a basic post-deploy health check.

Deploying (Render + Neon)

1. Spin up a free Neon Postgres project, grab the connection string.
2. Run migrate + seed against it once, locally:

   DATABASE_URL="<neon-connection-string>" npm run migrate
   DATABASE_URL="<neon-connection-string>" npm run seed
   

3. New Render Web Service, pointed at this repo:
   • Build: npm install && npm run build
   • Start: npm start
   • Env var: DATABASE_URL (same Neon string — don't set PORT, Render assigns its own and the server already reads process.env.PORT)
4. MCP endpoint ends up at https://<your-app>.onrender.com/mcp.

Seed data

checkout_reference	What it represents
chk_incident_001	Confirmed incident — captured, no order, buffer elapsed
chk_healthy_002	Healthy — order exists
chk_buffer_003	Too soon to tell — still inside the buffer window
chk_failed_004	Not eligible — payment was never captured

What's not done / what I'd do next

• Session state in server.ts is in-memory, which is fine for one instance but wouldn't survive a multi-instance deployment — would need a shared store (Redis, or similar) for that.
• Only one incident type exists right now. incident_type is already a column on incident_escalations specifically so more types can be added later without a schema migration.
• No auth on the MCP endpoint — intentional, per the assignment's scope guidance, but obviously not how this would ship in a real deployment.