Marketbasketanalysis MCP is answering right now. Last checked 12 min ago. 54 installs a week from npm. It exposes 19 tools.
Co-purchase intelligence and merchant ops tools for AI shopping, ecommerce, and B2B agents
The linked repository no longer exists on GitHub — it was deleted or made private.
Installs per day
peak 26 · avg 12 · -29% w/w
a month agotoday
Uptime history
50 days of history · worst day 99%
50 days agonow
100.0%
Uptime 24h
92 of 92 checks
19
Tools
read from the server
329 ms
Response time
average over 24h
54
Installs / week
npm and PyPI
What the code does
We have not read this server's code yet · tools taken from the live server
Capabilities
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
Package points at a different repository
48x-ai/marketbasketanalysis-mcp, 48x-ai/marketbasketanalysis
пакет @marketbasketanalysis/mcp ссылается на 48x-ai/marketbasketanalysis
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
This code can reach further than it looks
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
Connect this server
Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 12 min ago.
run in your terminal
claude mcp add marketbasketanalysis-mcp --transport http https://mcp.marketbasketanalysis.com/mcp~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"marketbasketanalysis-mcp": {
"url": "https://mcp.marketbasketanalysis.com/mcp"
}
}
}~/.codex/config.toml
[mcp_servers.marketbasketanalysis-mcp]
url = "https://mcp.marketbasketanalysis.com/mcp".cursor/mcp.json
{
"mcpServers": {
"marketbasketanalysis-mcp": {
"url": "https://mcp.marketbasketanalysis.com/mcp"
}
}
}.vscode/mcp.json
{
"mcpServers": {
"marketbasketanalysis-mcp": {
"url": "https://mcp.marketbasketanalysis.com/mcp"
}
}
}This one needs environment variables set before it will start:
MBA_API_KEY (Per-store MBA API key (mba_live_...), minted in the merchant admin), MBA_API_BASE (Override for self-hosted WooCommerce/Magento backends; defaults to the MBA hosted backend), MBA_PLATFORM (Store platform (shopify, bigcommerce, woocommerce, magento, orocommerce); drives per-platform path mapping and tool gating).
The author declared them in the registry entry; get the values from the project itself.
Available tools 19
Read directly from the server with tools/list, grouped by what they act on.
If a tool disappears, we record the date.
explain
explain_drift
Explain ONE drift alert: return its prior and current confidence (plus support, lift, and order sample count when the rule is still live) and a short plain-language narrative of how the pair moved versus the prior mining run. Use this when a merchant asks 'why did this pair drift?', 'explain this alert', or 'what changed for these two products?' after seeing it in get_drift_alerts. Different from get_drift_alerts: that lists the feed, this drills into a single alert_id with the change spelled out in a sentence. Handles a disappeared pair gracefully (only the prior confidence is available). BigCommerce only today.
explain_opportunity
Explain ONE mined opportunity: return its support, confidence, lift, and order sample count plus a short plain-language narrative of why the pair is a good cross-sell. Use this when a merchant asks 'why is this a good cross-sell?', 'explain this opportunity', or 'why should I bundle these?' after seeing it in get_opportunities. Different from get_opportunities: that lists the ranked set, this drills into a single opportunity_id with the stats spelled out in a sentence. Different from get_rationale: rationale is a generic pair 'why', this is the specific mined opportunity's own numbers. BigCommerce only today.
forecast
forecast_bundle
For an inventory, purchasing, or merchant-ops agent: forecast weekly sales and recommend a buy quantity for a specific bundle over a configurable horizon. Uses additive Holt-Winters on the bundle's stored historical sales (demand forecasting). Use this when the agent asks 'how many of bundle X should I order?', 'what should I stock for the next N weeks?', 'what's the demand outlook for bundle Y?', or 'forecast the next 8 weeks for the camera bundle'.
get_forecast_alerts
For an inventory or merchant-ops agent: list forecast-based alerts, the bundles with stockout risk, demand drop, demand spike, or an unreliable forecast curve. Use this when a merchant asks 'what's at risk of stockout?', 'which bundles are losing demand?', 'do I need to reorder anything?', or 'what should I restock?'. Pair with forecast_bundle to drill into a specific bundle.
score
score_cross_sell
Score the cross-sell strength (product affinity) between two specific products. Returns the confidence the merchant's real co-purchase data supports for the pair, or a clear 'no signal' result when there's no qualifying rule. Use this to validate a proposed pair before recommending it, or to answer 'is X a good cross-sell for Y?', 'how strong is the affinity between X and Y?', or 'how often are X and Y bought together?'.
score_return_risk
Predict return risk for a candidate bundle of 2-6 products. Returns the composite bundle return rate (max of items, since one returned item typically returns the whole bundle), each item's historical return rate, and a low/medium/high risk recommendation. Use this when the user asks 'will this bundle get returned?', 'predict return risk for these items', 'fashion bundle risk', 'is this set risky to ship together?', or when an agent is composing a bundle and wants to verify it won't tank the merchant's return KPIs. Backed by return-aware mining over the merchant's real order + refund history.
analyze
analyze_basket
Run market-basket analysis on a proposed basket / bundle to score its cohesion. Given 2+ products, returns a cohesion score 0..1 representing how strongly they bind together (their affinity) in the merchant's order data. Use this to vet a proposed bundle BEFORE recommending it, so agents can avoid suggesting bundles that look plausible but have no statistical signal. Also useful for 'is this a good bundle?', 'analyze this basket', or 'do these products go together?' questions.
bundle
get_bundle_for_cart
Given a list of products already in the cart, recommend products that frequently bundle with the cart to complete a high-confidence bundle. This is multi-item basket analysis for cart completion. Use when the user describes a multi-item cart and asks 'what else do I need?', 'what completes this set?', 'what's missing from this bundle?', 'recommend add-ons for this cart', or similar. Different from get_recommendations: this takes MULTIPLE products and returns items that pair with the cart as a whole, not single-item pairings.
drift
get_drift_alerts
For a merchant-ops or analytics agent: list active drift alerts, the recommendation rules whose confidence has materially changed (weakened, strengthened, disappeared, emerged) versus the prior mining job. Use this when a merchant asks 'what's changed?', 'is my model still accurate?', 'are any rules drifting?', or wants to investigate a SKU swap / seasonal shift.
execute
execute_weekly_plan_action
Execute a specific action from the merchant's weekly plan (publish bundle, run mining job, archive rule, etc.). Idempotent by action_id, safe to retry. Use this AFTER the merchant has confirmed which action from get_weekly_plan they want to run; do not call preemptively.
mine
mine_hui_itemsets
Run high-utility itemset (HUI) mining on a caller-supplied payload of orders + per-line unit_profit. Returns top-K itemsets ranked by aggregate utility (sum of profit across all occurrences). Use this when an agent needs to evaluate which item combinations drive the most profit (not just frequency) for a specific time window or product subset. Plus or Enterprise tier required on the merchant account.
opportunities
get_opportunities
List the merchant's ranked bundle / cross-sell opportunities mined from order history, with support / confidence / lift / revenue-weighted score. Use this when a merchant asks 'what are my top opportunities?', 'show me the best bundles I haven't published yet', or 'what should I prioritize?'. Pair with triage_opportunity to act on a specific one.
predict
predict_reorder
For a sales-rep or inventory / account-management agent: predict when a B2B customer / account is due to reorder. Returns predicted next-order dates for every SKU the customer has ordered >=2 times, with confidence based on the regularity of their cadence (reorder prediction / replenishment forecasting). Bucketed into 'overdue' / 'due_soon' / 'on_track' / 'not_predictable'. Use this when the agent asks 'what's Acme Corp due to reorder?', 'when will customer X need more of Y?', 'show me stockout risks for my B2B accounts', or for proactive replenishment workflows. Works on the Shopify, BigCommerce, WooCommerce, and Magento backends. Not available on OroCommerce.
propose
propose_subscription_bundle
Propose a recurring subscription bundle for a customer based on their first-order items. Given 1-5 seed products the customer has bought, returns a recurring subscription bundle (3-6 items) of the seeds plus complementary products, with a predicted cadence (median days between reorders), a 0..1 confidence score, and a rough monthly_value when prices are known. Use this when a merchant agent asks 'what should they subscribe to?', 'build a monthly subscription bundle from this order', 'propose a subscription bundle', 'recommend a recurring replenishment bundle', or 'what's the right subscription frequency for this customer?'. If a customer_id is supplied the tool blends in the customer's per-SKU reorder cadence; without one it falls back to the seed catalog cohesion alone. Works for Shopify, Magento, and WooCommerce merchants.
rationale
get_rationale
Fetch the one-sentence rationale for why product B is recommended alongside product A. Returns a short merchandiser-grade explanation ('these are commonly bought together by customers buying X') suitable for surfacing in a recommendation tile or chat reply. Use this after get_recommendations / get_bundle_for_cart when the agent or user asks 'why are these recommended together?' or 'explain this pairing'.
recommendations
get_recommendations
For a given product, recommend the top complementary, frequently-bought-together products customers also bought, based on mined order-history association rules. This is the single-product cross-sell tool. Use this when the user asks 'what goes with X?', 'what should I bundle with X?', 'what do customers also buy with X?', 'recommend products to cross-sell with X', or similar single-product co-purchase questions. Works for Shopify, Magento, and WooCommerce merchants.
substitutes
find_substitutes
For a given product, recommend the top substitute items that could REPLACE it (not complement it). Substitutes are the inverse of cross-sell: this answers 'what to buy instead', not 'what to buy with'. Use this when the user asks 'what's a substitute for X?', 'X is out of stock, what's a good alternative?', 'recommend a replacement for Y', 'find an equivalent product', or when a procurement agent needs to swap an unavailable SKU. Returns a ranked list with a similarity score and a reason (context_similar / category_match / vendor_match). Works for Shopify, Magento, and WooCommerce merchants.
triage
triage_opportunity
Pause, activate, or archive a specific opportunity from get_opportunities. State-mutating; guarded by confirm=true. Use this after the merchant has explicitly picked an opportunity to act on. Pass action='activate' to publish a proposed rule, 'pause' to temporarily hide an active one, 'archive' to permanently retire it.
weekly
get_weekly_plan
Fetch the current weekly action plan for the merchant: a ranked list of typed actions (publish opportunity, retire stale bundle, reorder inventory, investigate drift, etc.) the merchant should take this week. Use this when a merchant asks 'what should I work on this week?', 'what's on my plate?', 'show me my weekly plan', or wants a summary of pending tasks before opening the admin.
Endpoints
| URL | Transport | State | Latency | Checked |
|---|---|---|---|---|
| https://mcp.marketbasketanalysis.com/mcp | streamable-http | answering | 294 ms | 12 min ago |
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Marketbasketanalysis MCP — questions
Answers built from our own checks of this server.
What can Marketbasketanalysis MCP do?
It exposes 19 tools, read directly from the server on our last check. Among them: analyze_basket, execute_weekly_plan_action, explain_drift, explain_opportunity, find_substitutes, forecast_bundle and 13 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README.
How MCP servers expose tools in the first place →
What is Marketbasketanalysis MCP mostly used for?
Its tools cluster around score, forecast and explain. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.
Is Marketbasketanalysis MCP working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 92 of 92 checks got a reply (100.0%), average response time 329 ms. The bar chart above shows every period we have measured.
Is Marketbasketanalysis MCP still maintained?
The linked repository no longer exists on GitHub — it was deleted or made private. We show this because it changes what you can expect: an unmaintained server may keep answering for months and then stop without warning.
How do I connect Marketbasketanalysis MCP?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Marketbasketanalysis MCP need an API key?
No. Marketbasketanalysis MCP completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 19 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Marketbasketanalysis MCP?
It answers our handshake in 329 ms on average, which is faster than 41% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
How many people use Marketbasketanalysis MCP?
The npm package @marketbasketanalysis/mcp was installed 54 times in the last week. Week over week that is -29%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.