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48x-ai

@marketbasketanalysis/mcp

by 48x-ai

get_rationale

Fetch a one-sentence, merchandiser-grade explanation for why product B is recommended alongside product A, ready to display in recommendation tiles or chat replies.

Instructions

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'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_idYesThe base product id (the antecedent in the recommendation rule).
related_product_idYesThe recommended product id (the consequent in the rule).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations present, the description carries the behavioral disclosure burden. It clearly discloses the output type, length, tone, and example content ('short merchandiser-grade explanation'), which goes beyond the schema. It does not discuss failure modes or edge cases, but for a simple fetch-style tool this is largely sufficient.

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 three tightly written sentences with no filler. It front-loads the core purpose, then gives the return format and the invocation guidance. Every sentence earns its place.

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?

Given the tool's simplicity, no output schema, and two fully documented parameters, the description covers the essential context: what the explanation looks like, where it fits in a recommendation flow, and when to call it. Slightly more detail about output formatting or error behavior could push it higher, but nothing critical is missing.

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 schema already provides 100% coverage for both parameters, including their roles as antecedent and consequent. The description adds the product A/product B framing, which is helpful but largely restates what the schema already communicates, so it does not significantly raise the value above the schema baseline.

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 names a specific verb ('Fetch'), a specific resource ('the one-sentence rationale for why product B is recommended alongside product A'), and clearly distinguishes this from related sibling tools like get_recommendations and get_bundle_for_cart by focusing on the explanation step rather than the recommendation construction step.

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?

It explicitly states when to use this tool ('after get_recommendations / get_bundle_for_cart') and gives concrete trigger phrasings ('why are these recommended together?' or 'explain this pairing'), leaving no ambiguity about the intended invocation context.

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