You are looking at your Amazon ads dashboard. ACOS is up. You do not know which keyword is the problem. Your spend has crept 15 percent higher this week, but the console gives you a thousand rows of data and no clear story. So you export a spreadsheet, pivot it manually, cross-reference it against your search term report, and two hours later you have a theory. Maybe.
That workflow is broken. Not because spreadsheets are bad, but because the gap between raw advertising data and an actionable decision should not require two hours of manual labor every time ACOS moves. The information is already there. The bottleneck is the human reading it.
This article covers a different approach: connecting your AI coding agent directly to Amazon advertising data through the Model Context Protocol (MCP), so it can read the numbers, spot the patterns, and hand you the conclusion.
What MCP changes about PPC analysis
The Model Context Protocol lets LLMs talk to external data sources through a standardized interface. Instead of copying numbers from Amazon into a prompt and hoping the model interprets them correctly, the model queries the data directly. It sees the same numbers you see, in real time.
The sorftime-seller-agent MCP server exposes marketplace intelligence endpoints — including keyword-level advertising metrics — as tools an AI agent can call. The agent does not guess. It runs the query, reads the result, and reasons from actual figures.
Here is what that unlocks:
- Keyword-level spend analysis: which search terms are burning budget without converting.
- Automated keyword monitoring: track rank and performance changes over time without opening Seller Central.
- Competitor keyword tracking: see which terms competitors are bidding on and how their positioning shifts.
- Bid optimization suggestions: the agent cross-references ACOS, conversion rate, and impression share to flag overbid and underbid keywords.
Before and after: manual PPC analysis versus asking AI
Before (the manual way)
A seller notices ACOS trending up. The workflow looks like this:
- Log into Amazon Ads Console.
- Download the search term report for the last 30 days.
- Open in Excel, filter by campaign, sort by spend descending.
- Manually flag keywords where spend is high and orders are zero.
- Cross-reference those keywords against the targeting report to see who else is bidding.
- Decide which keywords to pause, which to lower bids on, which to test with exact match.
- Repeat weekly.
This takes 60 to 90 minutes per campaign, per week. A seller running five campaigns is looking at half a day of spreadsheet work that could be spent on product development, sourcing, or listing optimization.
After (asking AI)
With the MCP server running, the workflow becomes a single question. The seller types:
“Pull the keyword performance data for campaign ASIN B0XXXXXXX for the last 14 days. Flag any keywords where spend exceeds 15 dollars with zero attributed orders. For the keywords that are performing, suggest bid adjustments based on their ACOS relative to the 25 percent target. Also show me which competitor ASINs are showing up in the same search terms.”
The agent calls the relevant MCP tools, retrieves the structured data, performs the calculations, and returns a prioritized list. The seller reads the output, makes decisions, and moves on. Total time: under three minutes.
Here is what that interaction actually looks like when using the sorftime-seller-agent MCP with a terminal-based AI agent:
# Install and configure the MCP server
git clone https://github.com/DannylydST/sorftime-seller-agent
cd sorftime-seller-agent
python3 scripts/install.py
# Add to your AI agent's MCP configuration. For Claude Code, add to
# ~/.claude/mcp.json or the project .mcp.json:
# {
# "mcpServers": {
# "sorftime-seller-agent": {
# "command": "python3",
# "args": ["-m", "sorftime_seller_agent"],
# "env": {
# "SORFTIME_API_KEY": "your-key-here"
# }
# }
# }
# }
# Example prompt to your AI agent once MCP is connected:
# "Analyze keyword performance for my campaign targeting 'stainless steel water bottle'.
# Flag keywords with >$10 spend and zero conversions.
# Compare against competitor ASINs B0ABC123 and B0DEF456.
# Suggest 3 bid changes based on the data."
Example output: reverse-ASIN traffic terms for a competitor listing.
The agent processes structured keyword data, applies the thresholds, and outputs a table like this:
Keyword Spend Orders ACOS Recommendation
------------------------------ ------ ------- ------ ------------------------------
stainless water bottle 32oz $42.30 0 100% Pause — no conversions in 14d
insulated water bottle gym $18.70 1 65% Reduce bid by 20%
metal water bottle leak proof $12.40 3 18% Maintain — performing well
stainless steel bottle BPA free $28.90 1 88% Reduce bid by 30% or test exact
water bottle for hiking $6.20 2 12% Increase bid by 15% — low ACOS
Competitor keyword overlap:
- B0ABC123 bidding on 4 of your 12 tracked keywords, avg position 3.2
- B0DEF456 bidding on 7 of your 12 tracked keywords, avg position 2.1
The seller now knows, in one read, which keyword to pause, which to adjust, and which competitor is crowding their terms.
Automated keyword monitoring: set it and check in
One-off analysis solves the immediate problem. The bigger win is not needing to remember to run the analysis.
The MCP server can be wired into a scheduled loop. Configure your AI agent to check keyword performance daily and flag anomalies — a keyword that dropped from position 3 to position 12 overnight, a competitor that suddenly ramped bids on your top-converting term, a search term where spend doubled but orders stayed flat.
The setup is a cron entry or a loop command, depending on the AI agent platform. The point is that monitoring stops being something you do and starts being something that happens.
Competitor keyword tracking without the guesswork
Every Amazon seller wants to know what keywords competitors are targeting. The manual approach is reverse-engineering: search for the competitor’s product, note which search terms it ranks for, guess at their bid strategy. It is slow and incomplete.
The sorftime-seller-agent MCP surfaces competitor keyword data directly. For a given competitor ASIN, the agent can retrieve the keywords it ranks on, its estimated position for each, and changes over time. This transforms competitor keyword intelligence from speculation into a data feed.
The practical use case: a seller launches a new variation and wants to know which keywords the top three competitors are using to drive traffic. The prompt is straightforward:
“Show me the top 20 organic and sponsored keywords for ASINs B0ABC123, B0DEF456, and B0GHI789. Identify keywords where all three competitors rank but our ASIN does not. For those gaps, estimate the suggested bid to enter position 4-6.”
The agent returns a gap analysis. The seller reviews it and decides which keywords to target. No reverse-engineering, no manual searching, no guessing.
Where the real time savings come from
The value of connecting AI to advertising data is not that the AI is smarter than a seller. It is that the AI can read 5,000 rows of keyword data, apply rules consistently, and never get tired or distracted. The seller stays in control of strategy, thresholds, and final decisions. The AI handles the part that human brains are bad at: scanning large tables for patterns.
The time savings compound. A seller who checks PPC manually once a week and spends 60 minutes per session saves roughly 50 hours a year per campaign by delegating the data-reading step to an MCP-connected agent. Across three to five campaigns, that is 150 to 250 hours reclaimed.
Getting started
The sorftime-seller-agent MCP server is open source and free to use. Installation takes under five minutes.
git clone https://github.com/DannylydST/sorftime-seller-agent
cd sorftime-seller-agent
python3 scripts/install.py
The server runs locally. Your API keys and data stay on your machine. Connect it to any MCP-compatible AI agent — Claude Code, Cursor, Continue, or anything that speaks the MCP protocol.
A free account at open-intl.sorftime.com provides API access for marketplace intelligence across Amazon and other platforms. The MCP server ships with documentation for every available endpoint, plus example prompts to get started with PPC analysis, keyword research, and competitor tracking.