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Serveur MCP

marketbasketanalysis-mcp

io.github.48x-ai/marketbasketanalysis-mcp
Entreprise et opérations Commerce et retail Données et analytique Public et accessible MCP 2025-11-25

Ce que fait ce MCP

Analyzes order histories to recommend complementary products, bundles, substitutes, replenishment actions, forecasts, and merchant workflow priorities.

Capacités

Outils

19 outils
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.
Schéma d’entrée 
{'type': 'object', 'required': ['product_ids'], 'properties': {'product_ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'minItems': 2, 'description': 'The products in the proposed basket (2-6).'}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['action_id', 'confirm'], 'properties': {'confirm': {'type': 'boolean', 'default': False, 'description': 'Must be true to actually execute. Guard against accidental dispatch.'}, 'action_id': {'type': 'string', 'description': 'The id of the action to execute, from get_weekly_plan.'}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['alert_id'], 'properties': {'alert_id': {'type': 'string', 'description': 'The id of the drift alert to explain, from get_drift_alerts.'}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['opportunity_id'], 'properties': {'opportunity_id': {'type': 'string', 'description': 'The id of the opportunity to explain, from get_opportunities.'}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['product_id'], 'properties': {'limit': {'type': 'integer', 'default': 3, 'maximum': 6, 'minimum': 1, 'description': 'Maximum number of substitutes to return. Default 3, max 6.'}, 'product_id': {'type': 'string', 'description': "Product id, either the numeric storefront id (e.g. '8472918765') or the platform-specific GID/SKU. The id of the product the user wants to REPLACE."}}}
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'.
Schéma d’entrée 
{'type': 'object', 'required': ['bundle_id'], 'properties': {'bundle_id': {'type': 'string', 'description': 'Bundle identifier (the platform-specific bundle/kit id).'}, 'horizon_weeks': {'type': 'integer', 'default': 8, 'maximum': 52, 'minimum': 1, 'description': 'Forecast horizon in WEEKS. Default 8, range 1..52. The server converts this to days for the backend, so pass the number of weeks, not days.'}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['product_ids'], 'properties': {'limit': {'type': 'integer', 'default': 3, 'maximum': 6, 'minimum': 1, 'description': 'Max suggestions to return. Default 3, max 6.'}, 'product_ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 20, 'minItems': 1, 'description': 'List of product ids currently in the cart (numeric or GID/SKU).'}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': [], 'properties': {'limit': {'type': 'integer', 'default': 10, 'maximum': 50, 'minimum': 1, 'description': 'Max alerts to return. Default 10, max 50.'}, 'severity': {'enum': ['high', 'medium', 'low', 'all'], 'type': 'string', 'default': 'all', 'description': "Filter alerts by severity. Default 'all'."}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': [], 'properties': {'kind': {'enum': ['stockout_risk', 'demand_drop', 'demand_spike', 'unreliable_forecast', 'all'], 'type': 'string', 'default': 'all', 'description': "Filter by alert kind. Default 'all'."}, 'limit': {'type': 'integer', 'default': 10, 'maximum': 50, 'minimum': 1, 'description': 'Max alerts to return. Default 10, max 50.'}, 'severity': {'enum': ['high', 'medium', 'low', 'all'], 'type': 'string', 'default': 'all', 'description': "Filter by severity. Default 'all'."}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': [], 'properties': {'limit': {'type': 'integer', 'default': 10, 'maximum': 50, 'minimum': 1, 'description': 'Max opportunities to return. Default 10, max 50.'}, 'status': {'enum': ['proposed', 'active', 'paused', 'archived', 'all'], 'type': 'string', 'default': 'proposed', 'description': "Filter by opportunity status. Defaults to 'proposed' (untriaged)."}}}
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'.
Schéma d’entrée 
{'type': 'object', 'required': ['product_id', 'related_product_id'], 'properties': {'product_id': {'type': 'string', 'description': 'The base product id (the antecedent in the recommendation rule).'}, 'related_product_id': {'type': 'string', 'description': 'The recommended product id (the consequent in the rule).'}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['product_id'], 'properties': {'limit': {'type': 'integer', 'default': 3, 'maximum': 6, 'minimum': 1, 'description': 'Maximum number of recommendations to return. Default 3, max 6.'}, 'product_id': {'type': 'string', 'description': "Product id, either the numeric storefront id (e.g. '8472918765') or the platform-specific GID/SKU. Both are accepted."}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': [], 'properties': {}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['orders'], 'properties': {'top_k': {'type': 'integer', 'default': 20, 'maximum': 100, 'minimum': 1, 'description': 'How many top-utility itemsets to return. Default 20, max 100.'}, 'orders': {'type': 'array', 'items': {'type': 'object', 'required': ['order_id', 'items'], 'properties': {'items': {'type': 'array', 'items': {'type': 'object', 'required': ['sku', 'quantity', 'unit_profit'], 'properties': {'sku': {'type': 'string'}, 'quantity': {'type': 'number'}, 'unit_profit': {'type': 'number'}}}}, 'order_id': {'type': 'string'}}}, 'description': 'Order payload: each order has order_id + items[]. Each item has sku, quantity, unit_profit.'}, 'min_utility': {'type': 'number', 'description': 'Minimum utility threshold; itemsets below this are dropped.'}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['customer_id'], 'properties': {'product_id': {'type': 'string', 'description': "Optional: filter to a single product. Useful for 'when will customer X reorder product Y?'."}, 'customer_id': {'type': 'string', 'description': "The customer id on the store's own platform. Shopify accepts either the numeric storefront id (e.g. '7654321') or the full GID (gid://shopify/Customer/7654321). BigCommerce, WooCommerce, and Magento take their numeric customer id."}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['seed_product_ids'], 'properties': {'kit_size': {'type': 'integer', 'default': 4, 'maximum': 6, 'minimum': 3, 'description': 'Target total items in the subscription bundle (seeds + complements). Default 4, clamped to [3, 6].'}, 'customer_id': {'type': 'string', 'description': "Optional customer id (numeric storefront id or GID). When supplied, the tool pulls the customer's reorder-prediction history to anchor the cadence and confidence. Without this, the proposal uses seed-only catalog cohesion."}, 'cadence_days': {'type': 'integer', 'maximum': 180, 'minimum': 7, 'description': 'Optional target subscription frequency in days (e.g. 30 for monthly, 14 for biweekly). When supplied, the tool snaps the predicted cadence toward this target and weights candidates whose individual cadences are close to it.'}, 'seed_product_ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 5, 'minItems': 1, 'description': 'Products the customer bought in their first order (1-5). The proposed subscription bundle will include these plus complementary items.'}}}
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?'.
Schéma d’entrée 
{'type': 'object', 'required': ['product_a', 'product_b'], 'properties': {'product_a': {'type': 'string', 'description': "The 'antecedent' product (the one the customer already has)."}, 'product_b': {'type': 'string', 'description': "The 'consequent' product (the one being evaluated as a cross-sell)."}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['product_ids'], 'properties': {'threshold': {'type': 'number', 'default': 0.15, 'maximum': 1, 'minimum': 0, 'description': "Optional override for the 'high risk' cutoff. Defaults to 0.15 (15%). Items above this contribute to a stronger warning in the recommendation text. The low/medium/high classification itself uses fixed bands (<10% / 10-25% / >25%)."}, 'product_ids': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 6, 'minItems': 2, 'description': "Product ids for the candidate bundle. 2-6 items. Each id is either the numeric storefront id (e.g. '8472918765') or the platform-specific GID/SKU."}}}
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.
Schéma d’entrée 
{'type': 'object', 'required': ['opportunity_id', 'action', 'confirm'], 'properties': {'action': {'enum': ['activate', 'pause', 'archive'], 'type': 'string', 'description': 'What to do with this opportunity.'}, 'confirm': {'type': 'boolean', 'default': False, 'description': 'Must be true to dispatch. Guard against accidental triage.'}, 'opportunity_id': {'type': 'string', 'description': 'Opportunity id from get_opportunities.'}}}
Historique

Modifications récentes des outils

Ajouté
mine_hui_itemsets
17 September 2026 12:40
Ajouté
get_forecast_alerts
17 September 2026 12:40
Ajouté
explain_drift
17 September 2026 12:40
Ajouté
get_drift_alerts
17 September 2026 12:40
Ajouté
triage_opportunity
17 September 2026 12:40
Ajouté
explain_opportunity
17 September 2026 12:40
Ajouté
get_opportunities
17 September 2026 12:40
Ajouté
execute_weekly_plan_action
17 September 2026 12:40
Ajouté
get_weekly_plan
17 September 2026 12:40
Ajouté
forecast_bundle
17 September 2026 12:40
Ajouté
predict_reorder
17 September 2026 12:40
Ajouté
analyze_basket
17 September 2026 12:40
Ajouté
score_return_risk
17 September 2026 12:40
Ajouté
score_cross_sell
17 September 2026 12:40
Ajouté
propose_subscription_bundle
17 September 2026 12:40
Ajouté
get_bundle_for_cart
17 September 2026 12:40
Ajouté
get_rationale
17 September 2026 12:40
Ajouté
find_substitutes
17 September 2026 12:40
Ajouté
get_recommendations
17 September 2026 12:40
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