# Market Basket Analysis Muse connector

From musedirectory.ai, the independent directory of Meta Muse connectors. Not affiliated with Meta.

## Market Basket Analysis

Co-purchase intelligence for ecommerce merchants to recommend bundles, cross-sells, and substitutes based on order data.

- Record: https://musedirectory.ai/connector/market-basket-analysis
- Category: Shopping & Commerce
- Developer: 48x AI (https://www.marketbasketanalysis.com/docs/mcp)
- Muse status: Extra setup. Not in Muse's Connectors list yet. Muse can still use it: its page gives you a request to paste into Muse.
- Health: Working, 327ms, checked 2026-09-28T09:01:10Z
- Endpoint: https://mcp.marketbasketanalysis.com/mcp
- Auth: Needs an access key; Pricing: unknown
- Screening: Screened, no issues found (2026-09-24T02:46:32Z)
- Source: Found in the official MCP Registry (io.github.48x-ai/marketbasketanalysis-mcp) https://registry.modelcontextprotocol.io/v0/servers?search=io.github.48x-ai%2Fmarketbasketanalysis-mcp

Analyzes customer order history to recommend complementary products, substitutes, and subscription bundles. Helps merchants optimize pricing, inventory, and cart completion on Shopify, Magento, and WooCommerce. Includes return-risk scoring and demand forecasting.

Example request: "Show me what products customers frequently buy together with this camera so I can create a bundle."

How to connect: Not in Muse's Connectors list yet, but Muse can still use it. Paste this into Muse: "Use Market Basket Analysis to help me. It is a free service with an MCP server at https://mcp.marketbasketanalysis.com/mcp. It needs an API key from 48x AI; ask me to enter it through your secure credential prompt. Ask me before you share anything with it." Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.

Tools:
- 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
- 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, 
- 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_rec
- 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
- 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 re
- 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 recommendi
- 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 a
- 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, s
- 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 / re
- 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 ma
- 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
- 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.
- 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 
- 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 bun
- 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
- 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 accura
- 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
- 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 a
- 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 (

Screening checks:
- MCP handshake: pass (Answered in 376ms)
- Domain against threat feeds (Cloudflare security DNS): pass (mcp.marketbasketanalysis.com, www.marketbasketanalysis.com not flagged)
- Published packages against the OSV malicious-package database: pass (No malicious-package advisories)
- Hidden instructions or invisible characters in tool text: pass (19 tools read, nothing found)
- Inputs asking for passwords, card numbers or seed phrases: pass (None found)
- Domain and redirects: pass (Domain registered 3240 days ago)
- AI review of purpose and tool behavior: pass (No concerns)