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Datafiniti Product Search

datafiniti_product_search
Read-onlyIdempotent

Search Datafiniti's product database (250M+ records) — returns name, brand, categories, prices (amount/currency/merchant), and identifiers (UPC/ASIN/SKU). Uses the Datafiniti field:value query language. Example: datafiniti_product_search({ query: "brand:Nike AND categories:Shoes", num_records: 5, _apiKey: "your-token" })

Input Schema

NameRequiredDescriptionDefault
queryYesDatafiniti query, e.g. `brand:Nike AND categories:Shoes`. Fields: name, brand, manufacturer, categories, primaryCategories, prices, upc, asins, sku.
_apiKeyYesDatafiniti API token (from https://portal.datafiniti.co/). Sent as Authorization: Bearer.
num_recordsNoMax records to return (default 10, set 0 to get only num_found at no credit cost).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: it mentions the 250M+ record scale, the query language, and the return fields. It also implies the API key requirement via the example. No contradiction with annotations.

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 two sentences plus an example. It front-loads the core purpose, then gives the query language hint and a concrete example. Every sentence earns its place; no fluff or repetition of schema details.

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 read-only search tool with 100% schema coverage and no output schema, the description is quite complete. It covers what the tool does, the query language, an example, and the return fields. The only minor gap is that it doesn't explain the num_records=0 credit-saving behavior, but that's in the schema. The 250M+ scale and field list provide good context.

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

Parameters4/5

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

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds value by showing a complete invocation example with the query syntax and by listing the searchable fields (name, brand, manufacturer, categories, primaryCategories, prices, upc, asins, sku) which helps the agent construct valid queries. The num_records=0 behavior is in the schema, not the description, but the example clarifies usage.

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 states a specific verb ('Search'), a specific resource ('Datafiniti's product database'), and enumerates the return fields (name, brand, categories, prices, identifiers). It also distinguishes itself from sibling tools like datafiniti_business_search and datafiniti_property_search by explicitly naming the product database.

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

Usage Guidelines4/5

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

The description provides a concrete example of the query language and shows how to invoke the tool. It doesn't explicitly state when NOT to use it or name alternatives, but the context of 'product database' plus the sibling names (business_search, property_search) makes the usage context clear. The example with field:value syntax gives practical guidance.

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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