Seller Central already knows the business: orders, units, inventory, FBA quantities, settlements, fees, refunds, Brand Analytics search terms, and customer review themes.

The answers are in there. The operator still has to click through dashboards, download CSVs, request reports, wait, clean them up, and stitch the story together in a spreadsheet.

That breaks the moment the question is operational:

  • Which ASIN is about to stock out?

  • What did FBA and referral fees eat last month?

  • Which search terms are getting clicks but losing the sale?

  • What are customers complaining about on the cordless model?

  • Which products actually make money after fees?

If the answer takes two exports and a half day of analyst work, most teams stop asking.

That is why we built the Gentic Amazon MCP Server: connect your Amazon account once, then let an AI agent pull the data, store it, and answer in plain English.

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Download the Amazon Analyst Agent Builder Guide

Seller Central needs an agent interface

I've been saying the same thing across Shopify, Klaviyo, PostHog, Meta, reviews, and creative work: agents need access, not prompt tricks.

Amazon is the cleanest version of the argument.

Seller Central was designed for humans clicking around, not agents doing recurring analysis. SP-API is powerful, but using it directly means setup, roles, report requests, polling, parsing, and storage.

So most brands fall back to the same ritual: download a report, name it something like orders_may_final_v3.csv, send it to someone, maybe upload it to ChatGPT, and repeat next week because the file is stale.

That spreadsheet habit has a nicer chat box attached, but the operating system underneath hasn't changed.

The Amazon analyst agent works differently.

Your Amazon account sits on one side. Your AI agent sits on the other. The Gentic Amazon MCP Server sits between them and speaks SP-API for the agent. It can pull live data, request report-based data, load rows into GenticDB, and make the data queryable for future work.

The operator asks the question. The agent does the data work.

What it can answer

The first version is built around six jobs Amazon sellers already care about.

1. True profit per ASIN

Revenue is the number everyone stares at. Contribution margin is the number that tells the truth.

The agent joins order metrics, Amazon fees, refunds, and inventory cost so you can ask which products actually make money, which ASINs look good on sales volume but bad on margin, and what FBA or referral fees ate last month.

2. Stockout protection

Inventory misses cost more than today's order. They can cost the Buy Box, ranking, and momentum.

The agent reads FBA inventory by SKU and ASIN, including fulfillable, inbound, reserved, and researching quantities. Then it combines that with recent sales velocity to estimate days of cover.

The useful answer is not a table of quantities. It is a ranked list of risks with current stock, inbound stock, sales rate, and urgency.

3. Amazon SEO with real data

Most Amazon keyword tools are estimates. Useful, but still estimates.

Brand Analytics gives you Amazon's own search performance data: the queries driving impressions, clicks, and purchases for your products. The agent can request the Search Query Performance report, load it, and find the terms worth acting on.

High purchases deserve attention. High clicks with low conversion deserve a diagnosis. Maybe the price is wrong. Maybe the image is wrong. Maybe the query intent doesn't match the listing.

4. Finance reconciliation

Amazon's Finances feed is dense because the business is dense. Shipments, refunds, service fees, FBA fees, referral fees, adjustments, ad charges, and more all show up as event categories.

Ask where margin is actually going. Ask it to reconcile the last settlement against orders. Ask which fee categories increased the most this month.

The agent pulls financial events, summarizes the categories, keeps the raw events, and ties the biggest fee buckets back to the ASINs and orders that drove them.

No one should be eyeballing a settlement file and pretending the work is analysis.

5. Voice of customer

Amazon doesn't expose raw individual reviews through the API. That limit matters.

What Amazon does expose is still useful: aggregated review topics over a rolling roughly six-month window. Positive and negative themes. Mention counts. Star-rating impact. Representative snippets.

That lets the agent answer what customers dislike, what people praise, and which negative theme is hurting star rating the most. For product, listing, and roadmap decisions, this beats anecdote.

6. Your Amazon data, persisted verbatim

The first answer matters less than what happens after it.

When the agent pulls Amazon reports, rows land in your GenticDB lakehouse. Every source field is kept. Report windows are loaded idempotently, so re-pulling a window replaces that window cleanly instead of creating duplicate cleanup work. Order reads deliberately omit buyer PII such as names and addresses.

That turns the agent's work from a one-off answer into a queryable operating layer your next workflow can use.

Why the data layer matters

A dashboard shows you a view. A working data layer gives every agent the same durable base to read from.

An inventory agent checks stockout risk every Monday. A margin agent explains why net proceeds changed. A growth agent pulls Brand Analytics search terms and hands the findings to the listing and bids team. A product agent scans review themes and tells you what customers are saying in their own words.

All of that starts with the same foundation: real data, durable storage, agent-readable tools, and honest retrieval.

The tools underneath

The MCP server exposes nine tools:

  • amazon_connection_status checks account connection, marketplaces, currency, and suspended listings.

  • get_amazon_orders lists orders by date, status, or line item.

  • get_amazon_order_metrics returns units, sales, order count, and average unit price by time bucket.

  • get_amazon_fba_inventory returns fulfillable, inbound, reserved, and researching units by SKU and ASIN.

  • get_amazon_financial_events pulls finance events across 30+ categories.

  • request_amazon_report starts report generation.

  • get_amazon_report polls and loads report rows into GenticDB.

  • sync_amazon_reviews pulls aggregated review topics.

  • search_amazon_reviews searches those topics in natural language.

The report pair matters most. Amazon's richest data is not always real time. Brand Analytics, all-orders files, and merchant listing snapshots are report-based. Normally that means request, wait, download, parse, and clean. The agent turns that into two calls and a SQL-queryable table.

Five prompts to run first

Restock risk:
Check my FBA inventory. List every SKU where fulfillable plus inbound stock will run out within the next 14 days at last week's sales rate.

True margin:
For each of my top 10 ASINs by units last month, pull sales, average unit price, Amazon fees, and refunds. Show contribution margin per ASIN, sorted worst to best.

Amazon SEO:
Request the Brand Analytics Search Query Performance report for my top ASIN for the last 4 weeks. Show the top 20 queries by purchases and highlight high-click, low-conversion terms.

Finance reconciliation:
Pull all financial events from my last settlement period. Summarize fees by category, refunds, and net proceeds. Tie the largest fee categories back to ASINs.

Voice of customer:
Sync my Amazon review topics, then tell me the top negative themes across my catalog by mention count and star-rating impact.

None of these are exotic. That is the point. The best first agents do the obvious work teams already know they should do, but don't run often enough because the path from question to answer is too annoying.

Going live

Setup is easy: get SP-API credentials from Amazon, connect Amazon in Gentic, and point your agent to the Gentic MCP server.

The detailed setup guide is in the docs: gentic.co/amazon/docs.

Reply to this email or DM me on LinkedIn to let me know what you think.

– Bora

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