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

platepal_get_barcode_nutrition
Read-onlyIdempotent

Plate Pal. 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

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/openWorld/non-destructive, but with no output schema the description carries the return-value burden and does it well: per 100 g (or 100 ml) and per serving, traffic lights for fat/saturates/sugars/salt, Nutri-Score, allergens, Halal link. What it omits is behavior on invalid or unknown barcodes.

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 purpose, then trigger examples, then a compact enumeration of returned fields. The example-question list is longer than strictly necessary, but every sentence earns its place by clarifying when and what.

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 two-parameter read-only lookup with no output schema, the description supplies the missing return contract (units, traffic lights, Nutri-Score, allergens) and the intent triggers. Only gap is error/edge behavior for unknown barcodes.

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 coverage is 100%, so the schema already documents both 'code' (8-14 digits) and 'servings' (0.25-20). The description adds only unit framing ('per 100 g or 100 ml') and never mentions the servings aggregation parameter at all, so it does not go beyond the schema.

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?

States a specific verb-scope-resource: nutrition for a packaged product looked up by barcode, with UK traffic lights. The 'by barcode' / 'packaged product' framing distinguishes it from the sibling platepal_get_food_nutrition, which covers generic food lookup. An agent can pick between the two without opening a schema.

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?

Gives concrete trigger phrases ('is this barcode high in sugar?', 'after a scan', 'what's the salt in 5000157024886?') that tell the agent exactly which user intent maps here. It does not explicitly name when NOT to use it or point at a sibling alternative, so it stops short of a 5.

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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