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ScorpTech x402 Tools

Food Barcode

food_barcode
Read-onlyIdempotent

Food product facts by barcode (EAN-13, EAN-8, UPC-A, UPC-E; GTIN check digit validated) from Open Food Facts (ODbL): name, brand, quantity, categories, ingredients text, allergens and traces, additives, Nutri-Score grade, NOVA processing group, Green-Score, nutrients per 100 g (energy, fat, saturates, carbohydrates, sugars, fibre, protein, salt), labels, origin and image URL. Community data: always check the packaging for allergens. Price: $0.01 USDC per successful call (x402, Solana). Same tool over HTTPS: GET https://scorptech.it.com/api/x402/v1/food-barcode

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoPreferred language for names/ingredients, ISO 639-1 (default en)
barcodeYesEAN/UPC barcode digits (alias: code)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

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

The description adds substantial context beyond annotations: Open Food Facts provenance and ODbL license, community-data caveat about checking packaging for allergens, GTIN check-digit validation, and paid x402/Solana pricing per successful call. This is exactly the kind of operational and trust context an agent needs before invoking the 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?

The description is front-loaded with the core purpose, then packs identifiers, source, returned fields, caveats, and pricing into a single dense paragraph. It earns most of its length, but the long field list makes it less scannable than it could be.

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?

With no output schema, the description compensates by listing the returned data categories in detail, including nutrients, scores, allergens, additives, and image URL. It also covers source, licensing, data reliability, and payment, so an agent has enough context to call it correctly.

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 coverage is 100%, so parameter documentation is already strong. The description still adds useful semantics by naming accepted barcode standards and noting check-digit validation, though it does not add meaning for the lang parameter 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 and resource: retrieving food product facts by barcode from Open Food Facts. It clearly establishes the supported identifier formats and data domain, though it does not explicitly differentiate itself from any sibling tool.

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 implied by the barcode lookup purpose and supported formats, and the HTTPS endpoint is offered as an alternate access method. However, there is no explicit when-to-use guidance, prerequisites, or selection criteria versus alternatives.

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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