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edgargalvan

mise-en-place

by edgargalvan

get_nutrition

Retrieve verified macronutrients (kcal, protein, carbs, fat) for a specified ingredient and quantity, with transparent caveats for uncertain data.

Instructions

Get verified macros (kcal, protein, carbs, fat) for one ingredient at one quantity. fdc_id must come from a prior search_foods call -- never guess one. amount accepts grams/kg, oz/lb, or a count phrase like "1 medium onion" (the server resolves counts through a cited chain of real sources; it will raise rather than guess if it cannot). label is the user's own word for this ingredient, written verbatim -- do not paraphrase or clean it up, it is used to judge how confident this match is. query should be a search string that actually returns fdc_id among its results -- the server re-runs it and checks. This catches an ID invented from memory; it does not confirm query is the search you actually used, only that fdc_id is findable through it right now.

Open Food Facts fallback: set source to whatever search_foods returned for the candidate you chose ("usda", the default, or "off"). For "off", fdc_id holds the Open Food Facts barcode search_foods already gave you, query is not consulted (no re-verification exists yet for this source), and count-phrase quantities only resolve if the product itself states a serving size -- otherwise give an explicit mass (grams/oz). The response always carries a caveat naming this a crowdsourced, not USDA, source.

The response always includes a summary, a report_path, and totals (the plate sum, computed server-side, even for one item and even with no target -- never add it up yourself). Read the summary in full and relay every flagged item to the user, not just the confirmed ones -- a caveat you drop silently (a low-confidence match, an assumed mass) defeats the only safety mechanism this tool has. A macro genuinely absent from a food (no protein data at all, for example) comes back as null with a caveat naming it, never as zero -- zero means the food truly has none.

The macros this tool returns are the only ones to state as fact for a food it matched -- never invent or estimate your own number in their place. The one narrow exception: if search_foods finds nothing at all for an item, you may estimate that single item's nutrition instead of leaving it out, but only if you say plainly it is your own guess, not verified data (C-28).

Pass target if the user has a kcal/protein/carb/fat goal for this lookup; use the returned totals.outcomes, never compute whether a target was met yourself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reqYes
targetNo
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and succeeds. It discloses query re-verification, server-side count resolution, 'raise rather than guess' behavior, server-computed totals, caveat-relay requirements, null-vs-zero macro semantics, and the rule against inventing numbers. No contradiction.

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 long but front-loaded with the core contract and organized by topic. Nearly every sentence carries safety-critical instruction, though some repetition around server-side computation and the 'never invent' rule could be tightened. Length is mostly justified.

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?

There is no output schema, yet the description pre-defines the response shape (summary, report_path, totals), caveat handling, and failure modes (unresolvable counts, null macros, unverifiable queries, source caveats). It leaves no ambiguity about how to interpret results or handle edge cases.

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

Parameters5/5

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

Schema description coverage is 0%, but the description compensates thoroughly. It explains fdc_id provenance, amount unit syntax and count phrases, label verbatim requirements, query verification, source values, and target usage. Every relevant input is given meaningful guidance beyond the bare 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?

The first sentence states the exact operation: 'Get verified macros (kcal, protein, carbs, fat) for one ingredient at one quantity.' The singular 'one ingredient at one quantity' clearly distinguishes this from the batch sibling. It names the resource and the specific data returned.

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

Usage Guidelines5/5

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

The description explicitly says fdc_id must come from a prior search_foods call and never be guessed. It provides source-specific handling for 'off', a fallback policy when search_foods finds nothing, and clear conditions around count-phrase quantities. This gives explicit when-to-use and when-not-to-use 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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