Example output: competitor keyword strategy, pricing history, and category viability assessment.You are staring at a competitor’s listing. They are ranking #1. You need to know: what keywords drive their traffic, how often they change price, what their review sentiment reveals, which variations sell fastest, and whether their brand presence in the category is strong enough to make competing impractical. Right now, getting those answers means opening four or five tools, cross-referencing data manually, and hoping you did not miss a signal buried in a spreadsheet column.
That process is not research. It is data assembly. The actual thinking — deciding what the information means for your business — happens only after you have spent 20 to 40 minutes gathering it.
The sorftime-seller-agent changes the order of operations. Instead of assembling data and then thinking, you ask your AI agent one question and start thinking immediately.
Here is what competitive intelligence looks like when your AI agent connects directly to marketplace data through MCP.
You open your AI tool — Claude Code, Codex CLI, Cursor, or any MCP-compatible agent — and type:
Analyze competitor ASIN B0XXXXXXXXX on Amazon US.
Give me their keyword strategy, pricing history over the last 90 days,
review sentiment breakdown, variation performance, and an assessment
of whether this subcategory is worth entering for a new seller.
Example output: competitor keyword strategy, pricing history, and category viability assessment.
Your AI agent does not give you a generic response from training data. It calls the sorftime-seller-agent MCP server, which queries live marketplace data across multiple endpoints, and structures the results into something you can actually use:
What would have been a 30-minute manual workflow — opening keyword tools, exporting CSV files, scraping review pages, cross-checking Keepa charts, and building your own comparison table — now takes under 30 seconds.
You are not outsourcing the decision. You are outsourcing the grunt work that delays the decision.
Traditional seller tools are built around a dashboard model: humans click menus, select filters, read tables, and export data. Each tool covers part of the puzzle. The keyword tool knows nothing about pricing history. The review analyzer cannot cross-reference keyword rankings. The BSR tracker has no opinion about brand concentration. You, the seller, are the integration layer — and integrating data across tools is where time evaporates.
MCP flips the model. Instead of the human navigating tools, the AI agent navigates the data. You describe what you need. The agent decides which data sources to query, calls them in parallel when possible, and synthesizes the results.
The sorftime-seller-agent provides the bridge. It exposes 86 marketplace intelligence functions — product details, keyword analysis, category scouting, competitor profiling, profit estimation — as tools any MCP-compatible AI agent can use. The AI handles the plumbing. You handle the strategy.
Installation is one command. You need Python 3.10 or later.
git clone https://github.com/DannylydST/sorftime-seller-agent && cd sorftime-seller-agent && python3 scripts/install.py
The installer sets up a virtual environment, installs dependencies, and configures the MCP server for your AI agent. You will be prompted for a Sorftime MCP key, available free at open-intl.sorftime.com. The key stays on your machine.
After installation, restart your AI agent. It auto-discovers the MCP tools. From that point forward, any marketplace question you ask can be answered with live data instead of model guesses.
The sorftime-seller-agent spans the competitive intelligence workflow end to end:
| Analysis Area | What the Agent Can Query |
|---|---|
| Keywords | Search volume, organic ranking position, traffic-driving terms per ASIN, long-tail gap analysis |
| Pricing | Historical price points, discount patterns, price-to-BSR correlation |
| Reviews | Sentiment clustering, velocity tracking, recurring buyer feedback themes |
| Variations | Per-child performance, concentration analysis, review distribution across variants |
| Category landscape | Brand concentration ratios, new-entrant dynamics, top-20 review benchmarks |
| Profit estimation | FBA fee calculation, breakeven analysis, margin sensitivity to price changes |
The tool works across multiple marketplaces, not just Amazon US. The same MCP functions query Amazon, Walmart, TikTok Shop, Shopee, TEMU, and 1688 — over 160 analysis dimensions across all supported platforms.
The sorftime-seller-agent does not tell you whether to enter a market. It does not predict sales, guarantee rankings, or eliminate business risk. It provides structured data and surface-level analysis so you can make informed decisions. Marketplace data has inherent limits — sales figures are modeled, not reported, and category snapshots reflect a moment in time rather than real-time streams. The tool flags what is uncertain rather than pretending it is precise.
The quality of your outcome still depends on your understanding of your market, your supply chain, your unit economics, and your customers. What the tool changes is how much time you lose to data wrangling before you get to the part that requires actual judgment.
git clone https://github.com/DannylydST/sorftime-seller-agent && cd sorftime-seller-agent && python3 scripts/install.py
After installing, ask your AI agent:
Analyze the top 3 competitors in [your category] on Amazon US.
What are their keyword advantages, pricing patterns, and review gaps?
Which one is most vulnerable to a new entrant?
A free MCP key is available at open-intl.sorftime.com. No credit card required.
[1] Sorftime Seller Agent GitHub Repository — https://github.com/DannylydST/sorftime-seller-agent
[2] Sorftime International MCP Portal — https://open-intl.sorftime.com