ForkMate

lookup_barcode

Read-only

Look up a packaged food by its UPC/EAN barcode via Open Food Facts. IMPORTANT: the macros are PER 100 g (see serving) — scale to the portion eaten before logging with log_meal. It MAY also carry serving_grams/serving_label for one household serving — offer 'N servings' when present, still scaling the per-100 g macros before logging. Pass the returned source to log_meal to preserve provenance. SAFETY: all calorie and macro values here — including carbohydrates — are ESTIMATES (from USDA / Open Food Facts or the user's own entry), approximate and not lab-measured or per-batch. They are for general nutrition tracking ONLY. Do NOT use them for insulin dosing, carb-counting for a bolus, blood-glucose prediction, or any other medical decision, and do NOT compute a dose or glucose estimate from them. For anything medical, direct the user to the product's own label and their care team.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`upc`	Yes	UPC/EAN barcode, digits only (8–14 digits).

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description goes further by disclosing that macros are estimates (not lab-measured), are per 100g, and include a safety warning about medical use. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy but deliberately structured with purpose, scaling instructions, and a dedicated SAFETY section. Every sentence provides essential guidance for a nutrition/health tool, though it borders on verbose.

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?

For a tool with no output schema, the description compensates fully: it names key return fields (macros, serving_grams, serving_label, source), explains scaling logic, and provides safety guardrails. This is complete for the tool's complexity.

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?

The input schema fully describes the single parameter (upc: digits only, 8-14 digits). The description adds context about result interpretation (per 100g, serving_grams) but no new parameter-specific details, so the baseline 3 applies.

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 clearly states the tool's function: 'Look up a packaged food by its UPC/EAN barcode via Open Food Facts.' This is a specific verb (look up) and resource (packaged food barcode), and it distinguishes itself from siblings like search_foods and log_meal.

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?

It gives clear usage context: use for barcode lookup, scale per-100g macros before logging, pass source to log_meal, and includes a strong safety caveat about not using for medical decisions. It doesn't explicitly contrast with search_foods but the barcode-specific scenario is unambiguous.

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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TDQS

A4.5/5.0

Disambiguation5/5

Each tool targets a distinct resource and action: pantry vs diary vs food search vs preferences vs diagnostics. Even similar tools like get_day/get_range are clearly differentiated by scope, and search_foods/lookup_barcode are distinguished by input type (text vs barcode).

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with lowercase snake_case (add_pantry_item, get_day, log_meal, search_foods). The only exception is whoami, which is a standard diagnostic convention and does not disrupt the overall consistency.

Tool Count5/5

With 12 tools, the server is well-scoped for its food-tracking domain. Each tool covers a necessary function (pantry CRUD, diary CRUD, food search, preferences, diagnostics) without redundancy or bloat.

Completeness5/5

The tool set provides full lifecycle coverage for the core domain: pantry items can be added, read, and removed (upsert covers update); diary entries can be created, read (single/day/range), updated, and deleted; food lookup includes text search and barcode; and preferences are accessible. No obvious gaps hinder agent workflows.

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