A seller running on Amazon US also lists on Walmart. Their team is testing TikTok Shop. The ops lead monitors Shopee across three Southeast Asian markets. Every morning starts with eight browser tabs, five logins, and a spreadsheet that cross-references data manually stitched together from half a dozen dashboards.
That is not a hypothetical. It is the day-to-day reality for sellers scaling beyond a single platform. Each marketplace has its own interface, its own data format, its own refresh cadence. The friction is not in any one tool — it is in the toggling itself. Context switching eats time, introduces errors, and makes trend-spotting across platforms a manual slog.
The Real Cost of Dashboard Switching
Consider a typical morning routine for a multi-platform seller:
Before: Open Amazon Seller Central. Navigate to Brand Analytics. Export a CSV. Open Walmart Seller Center. Find the equivalent report — different column names, different date ranges. Open TikTok Shop Seller Center. Check GMV and live-stream performance. Copy numbers into a spreadsheet. By the time the data is in one place, it is already hours old, and the seller has done exactly zero analysis.
Every additional marketplace compounds the problem. Add Shopify. Add eBay. Add Mercado Libre for Latin America. Each new platform adds another login, another interface to learn, another export format to normalize. The seller becomes a data-entry operator instead of a strategist.
After: A single command. One interface. The same query syntax across 40+ marketplaces. Data lands in a consistent format, ready for analysis or feeding into an AI agent for pattern detection. The seller spends the morning on decisions, not data wrangling.
One Terminal, Forty Marketplaces
The sorftime-seller-agent is an open-source MCP (Model Context Protocol) tool built for marketplace intelligence. It exposes structured data from over 40 e-commerce platforms through a single CLI — Amazon (19 global marketplaces), Walmart, TikTok Shop, Shopee, TEMU, Lazada, Mercado Libre, and more. No browser tabs. No dashboards. No spreadsheets stitched together by hand.
Because it speaks MCP, any compatible AI agent — Claude Code, Codex CLI, Cursor, Windsurf, or a custom agent — can load the tool and operate across all connected marketplaces with the same command patterns. The AI does not care whether the data comes from Amazon Japan or Walmart Canada. It gets the same structured response and works on it the same way.
What a Single Command Looks Like
Here is a real example. To pull product intelligence for an ASIN on Amazon US:
sorftime product asin --marketplace US --asin B0EXAMPLE
Now do the same thing for a product on Walmart:
sorftime product walmart --marketplace US --item-id 123456789
The commands live side by side in a script. A seller can query an ASIN, pull its keyword rankings, check BSR movement across categories, and repeat the entire sequence for a Walmart item — all from the same terminal, with output formatted identically.
For sellers tracking cross-platform performance, this is the difference between a 90-minute manual workflow and a 30-second script.
Scripting a Cross-Platform Morning Routine
The real power emerges when commands are composed into a single repeatable workflow. Here is an annotated example of a morning check script:
#!/bin/bash
# Cross-platform product health check — runs in under 60 seconds
echo "=== Amazon US — Top 5 SKUs ==="
for asin in B0AAA B0BBB B0CCC B0DDD B0EEE; do
sorftime product asin --marketplace US --asin "$asin" \
| jq '.data | {title, price, bsr_main, rating, review_count}'
done
echo "=== Walmart US — Priced Items ==="
for item_id in 111 222 333; do
sorftime product walmart --marketplace US --item-id "$item_id" \
| jq '.data | {title, price, sales_rank}'
done
echo "=== TikTok Shop — GMV Snapshot ==="
sorftime tiktok shop-performance --marketplace US --shop-id YOUR_SHOP_ID \
| jq '.data | {gmv_today, order_count, top_live_room}'
Each block queries a different platform. The output format is consistent. The entire script can be dropped into a cron job for automated daily snapshots, fed into a dashboard, or piped directly to an AI agent for anomaly detection.
Built for the Agent Era
The tool is designed as an MCP server first. This means any MCP-compatible AI agent can interact with it natively — not through a separate API layer, not through screen scraping. The agent queries a marketplace, receives structured data, and reasons about it in the same conversation.
A Claude Code session can pull keyword data, compare it against historical trends, and flag which products need attention — all without the seller opening a browser. A Codex CLI agent can monitor competitor price changes across platforms on a schedule and alert the seller when something moves. The tool does not force a specific AI platform or workflow. It provides the data interface and gets out of the way.
Coverage That Grows With the Seller
The tool covers over 40 marketplace endpoints across six domains:
| Domain | Coverage | Example Data Points |
|---|---|---|
| Amazon | 19 marketplaces (US, EU, JP, AU, and more) | ASIN intelligence, keyword ranking, BSR tracking, category penetration, review analysis, sub-ASIN sales estimates |
| Walmart | US marketplace | Product detail, pricing, sales rank, category data |
| TikTok Shop | US, UK, Southeast Asia | Shop performance, live-stream GMV, video analytics, creator discovery |
| Shopee | 7 Southeast Asian markets | Product search, category data, shop analytics |
| TEMU | US and global | Product listings, pricing trends, category data |
| 1688 & More | China wholesale, Lazada, Mercado Libre, and additional platforms | Sourcing prices, competitive benchmarks, regional market data |
A seller starting on Amazon US can add platforms incrementally without changing tools or learning a new interface each time.
It Does Not Replace Judgment — It Removes the Drudgery
Marketplace data tools exist in abundance. The problem is rarely access — it is integration. A seller already has the data. The bottleneck is the gap between “I know this number exists somewhere” and “I have this number in a usable format alongside its counterparts from other platforms.”
sorftime-seller-agent closes that gap. It moves data from scattered dashboards into a single structured interface that both humans and AI agents can read. A seller still makes the calls — which products to launch, which keywords to target, which markets to enter or exit. The tool just makes sure the relevant data is in one place, at the right time, in the right format, with zero manual assembly required.
The open-source model means the code is inspectable, the data flow is transparent, and there is no vendor lock-in. Sellers can extend the tool, fork it, or integrate it into existing internal systems.
Getting Started
The tool installs with a single script and connects to any MCP-compatible agent in minutes.
Try it:
git clone https://github.com/DannylydST/sorftime-seller-agent
cd sorftime-seller-agent
python3 scripts/install.py
Free access at open-intl.sorftime.com.