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48x-ai

@marketbasketanalysis/mcp

by 48x-ai

get_bundle_for_cart

Identify missing products that complete a shopper's bundle by analyzing the entire cart contents and recommending items frequently bought together with all cart items.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax suggestions to return. Default 3, max 6.
product_idsYesList of product ids currently in the cart (numeric or GID/SKU).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.0

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does disclose the analytical behavior (multi-item basket analysis for cart completion, whole-cart pairing) and implies a non-mutating read operation, but it does not explicitly state that nothing is modified, how results are scored, or what happens with invalid product IDs. This is acceptable but leaves some behavioral details implicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, then gives concrete usage signals, then differentiates from a sibling tool. No sentence is wasted, and the structure makes it easy for an agent to process quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with only two parameters and no output schema, the description covers the essential invocation context: what input is expected, when to use it, and how it differs from the closest sibling. It does not describe the return structure, but the absence of an output schema makes that a minor gap rather than a critical one.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents both parameters with 100% coverage, including defaults, ranges, and accepted formats. The description adds useful context that the cart must contain MULTIPLE products and that pairing is whole-cart, but it does not significantly deepen the parameter semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('recommend') and resource ('products that frequently bundle with the cart'), and clearly frames this as multi-item basket analysis for cart completion. It explicitly distinguishes itself from get_recommendations by emphasizing MULTIPLE products and whole-cart pairing, so an agent can tell them apart.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit trigger phrases ('what else do I need?', 'what completes this set?', etc.) and states when it should be used: when a user describes a multi-item cart. It also provides a when-not/alternative point by explaining the difference from get_recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.