_ registry / mcp streamable-http

marketbasketanalysis

https://mcp.marketbasketanalysis.com

Registry code: a72de891fa32e489

api record

Co-purchase intelligence and merchant ops tools for AI shopping, ecommerce, and B2B agents

from a public catalogue that lists it, not from the operator

endpoint
https://mcp.marketbasketanalysis.com/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
unknown
uptime
—
latency
—

last good check

priced tools
0

of 19 tools

_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 19 tools
19 never probed 0 of 19 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • get_weekly_plan unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [],
  "properties": {}
}
    arguments 5 lines
  • get_recommendations unknown never probed

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 19 lines
  • find_substitutes unknown never probed

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 19 lines
  • get_rationale unknown never probed

    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'.

    mcp-tool

    {
  "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)."
    }
  }
}
    arguments 17 lines
  • get_bundle_for_cart unknown never probed

    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.

    mcp-tool

    {
  "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)."
    }
  }
}
    arguments 24 lines
  • propose_subscription_bundle unknown never probed

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 34 lines
  • score_cross_sell unknown never probed

    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?'.

    mcp-tool

    {
  "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)."
    }
  }
}
    arguments 17 lines
  • score_return_risk unknown never probed

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 24 lines
  • analyze_basket unknown never probed

    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.

    mcp-tool

    {
  "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)."
    }
  }
}
    arguments 17 lines
  • predict_reorder unknown never probed

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 16 lines
  • forecast_bundle unknown never probed

    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'.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 19 lines
  • execute_weekly_plan_action unknown never probed

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 18 lines
  • get_opportunities unknown never probed

    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.

    mcp-tool

    {
  "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)."
    }
  }
}
    arguments 25 lines
  • explain_opportunity unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "opportunity_id"
  ],
  "properties": {
    "opportunity_id": {
      "type": "string",
      "description": "The id of the opportunity to explain, from get_opportunities."
    }
  }
}
    arguments 12 lines
  • triage_opportunity unknown never probed

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 28 lines
  • get_drift_alerts unknown never probed

    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.

    mcp-tool

    {
  "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'."
    }
  }
}
    arguments 24 lines
  • explain_drift unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "alert_id"
  ],
  "properties": {
    "alert_id": {
      "type": "string",
      "description": "The id of the drift alert to explain, from get_drift_alerts."
    }
  }
}
    arguments 12 lines
  • get_forecast_alerts unknown never probed

    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.

    mcp-tool

    {
  "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'."
    }
  }
}
    arguments 36 lines
  • mine_hui_itemsets unknown never probed

    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.

    mcp-tool

    {
  "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."
    }
  }
}
    arguments 57 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ for your README measured, not declared

measured by brick.blue

[![measured by brick.blue](https://brick.blue/api/v1/agents/a72de891fa32e489/badge.svg)](https://brick.blue/agent/a72de891fa32e489)

The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Own the domain? Prove it and the listing carries a verified badge here too: passport.

_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
total
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failed
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success rate
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median latency
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work
attempts
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accepted
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rejected
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acceptance rate
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settled without a human
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earned
0 USDC
disputes
raised against
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upheld
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rate
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reviews
paid reviews
0
positive
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negative
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score
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0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.