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NeuralVerge Business Intelligence

Target product recommendations

run_target_products_recommendations

Related products Target shows on a product page: alternatives, guests also viewed, complementary, deals, top performing, new arrivals, similar. 1 point per returned record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoRecommendation type. Options: alternatives = Alternatives, also_viewed = Guests also viewed, complementary = Complementary, deals = Deals, top_performing = Top performing, newness = New arrivals, similar = Similar
productYesProduct (TCIN or URL). e.g. 86216189
max_itemsNoMax items (up to 500) Default: 25.
start_pageNoStarting page

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.4/5.0
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 one real behavioral trait — cost of '1 point per returned record' — and the data source (Target product pages). However, it says nothing about pagination behavior (despite a start_page param), rate limits, or return shape, leaving meaningful gaps for an unannotated tool.

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?

One dense sentence with zero filler; the scope ('related products Target shows') is front-loaded and the category list follows. Only slight weakness is that the cost sentence is a fragment rather than fully integrated, but nothing is wasted.

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

Completeness3/5

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

With no output schema and no annotations, the description must carry more weight. It adequately conveys what is returned (the seven recommendation types) and the cost model, but omits any pagination or result-volume guidance an agent would need for the start_page/max_items parameters.

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%, so the type enum, product (TCIN/URL), max_items, and start_page are already fully documented in the schema. The description merely re-lists the recommendation types already present in the enum, adding no syntax or format meaning beyond the schema.

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

Purpose4/5

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

The description states a specific verb+resource: it returns 'related products Target shows on a product page' and enumerates the recommendation categories. This clearly distinguishes it from run_target_products (details), run_target_products_reviews, and run_target_products_search, though it never names those siblings explicitly.

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

Usage Guidelines3/5

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

Usage is only implied — an agent can infer this is for pulling recommendation/related-product listings on a Target product page. There is no explicit when-to-use, when-not-to-use, or reference to an alternative sibling (e.g., use run_target_products for full product detail).

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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