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marketbasketanalysis-mcp

by io.github.48x-ai

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of substitutes to return. Default 3, max 6.
product_idYesProduct 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.

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses that the tool returns a ranked list with a similarity score and a reason (context_similar / category_match / vendor_match), plus supported platforms. It doesn't mention read-only status or error behavior, but it adds substantial behavioral context beyond a bare description.

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

Conciseness4/5

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

The description is front-loaded with the core purpose and then covers usage, return behavior, and supported platforms. Every sentence adds value, though 'REPLACE it (not complement it)' and 'inverse of cross-sell' are slightly redundant.

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

Completeness5/5

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

For a simple 2-parameter tool with no output schema, the description is complete: it states when to use it, what it returns, the reason categories, and platform support. Nothing critical for correct invocation 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?

Schema description coverage is 100% for both parameters, so the schema already fully documents product_id and limit. The description adds little parameter-level detail beyond restating that product_id identifies the item to replace, which is already in the schema. Baseline 3 is appropriate.

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 and resource: 'recommend the top substitute items that could REPLACE it'. It explicitly contrasts with complement/cross-sell, which clearly separates it from related siblings like score_cross_sell and get_recommendations.

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?

Provides explicit usage triggers with concrete query examples ('what's a substitute for X?', 'out of stock', 'find an equivalent product') and states the when-not case ('not complement it', 'inverse of cross-sell'). This is strong routing guidance for an agent.

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

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TDQS

A4.1/5.0
Disambiguation4/5

Tools map largely one-to-one to distinct actions, and descriptions explicitly differentiate similar-looking pairs like get_recommendations vs. get_bundle_for_cart vs. find_substitutes. However, score_cross_sell vs. analyze_basket and get_rationale vs. explain_opportunity could still confuse an agent, since both score/explain overlapping concepts. Overall ambiguity is low but not zero.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: get_* for retrieval, explain_* for drill-downs, score_* for validations, and action verbs for state changes. No camelCase or mixed conventions are present, making the naming highly predictable.

Tool Count3/5

19 tools is on the heavy side for a single server and exceeds the typical 3–15 range, even though most tools have distinct purposes. The large count reflects a broad feature set covering recommendations, alerts, forecasting, reorder, returns, and weekly planning, but a few tools could be consolidated. It feels slightly bloated rather than egregiously so.

Completeness4/5

The surface covers the main discovery–evaluation–action workflow: recommend, score, explain, triage, plan, and forecast. Minor gaps exist—there is no direct tool for creating or editing a bundle outside of weekly-plan actions, and no catalog-browsing capability—but those are largely external concerns. The workflow is coherent with no major dead ends.

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