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Nutrition for a packaged product by barcode, with UK traffic lights

get_barcode_nutrition
Read-onlyIdempotent

Nutrition for a packaged product by barcode, with UK traffic lights. For requests like "is this barcode high in sugar?", "how many calories in this?" after a scan, or "what's the salt in 5000157024886?". Returns per 100 g (or 100 ml) and per serving values, traffic lights for fat, saturates, sugars and salt, Nutri-Score, allergens and a Halal or Not? link for checking the ingredients.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesBarcode digits (8 to 14).
servingsNoNumber of servings to total up (0.25 to 20).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint=false, so the safety profile is covered. The description adds real value beyond that by disclosing the return payload: per 100 g/ml and per serving values, traffic lights for fat/saturates/sugars/salt, Nutri-Score, allergens and a Halal link. It does not say what happens with an unknown barcode, but for a read-only lookup that is a minor gap.

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?

Front-loaded with the purpose, then usage examples, then return contents in three tight sentences with no filler. The opening sentence duplicates the title verbatim, a minor redundancy, but everything else 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?

With no output schema, the description carries the burden of explaining return values and does so thoroughly (units, traffic lights, Nutri-Score, allergens, Halal link). The parameter surface is small and fully covered by the schema, so the definition is nearly complete; only failure/edge-case behaviour is unaddressed.

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%, with both the barcode format (8-14 digits) and the servings range already documented in the schema, so the baseline is 3. The description's mention of 'per serving values' loosely ties to the servings parameter but repeats what the schema description ('number of servings to total up') already conveys, adding little new meaning.

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?

States a specific verb+resource+scope: nutrition for a packaged product retrieved by barcode, with UK traffic lights. This scope implicitly separates it from the broader get_food_nutrition and get_meal_ideas siblings, but the description never names them, so the differentiation is inferential rather than explicit.

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?

Three concrete example requests ('is this barcode high in sugar?', 'how many calories in this?' after a scan, 'what's the salt in 5000157024886?') give clear situational context for when to reach for this tool. There are no stated exclusions or explicit pointers to the sibling tools, so it stops short of a full routing rule.

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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