mise-en-place

An MCP server that returns trustworthy macronutrient data for a named ingredient at a named quantity, sourced from USDA FoodData Central.

It has one job: when an LLM is planning a meal or building a recipe, give it real calories/protein/carbs/fat for a real food at a real quantity, never a plausible-sounding guess. Composition, recipe design, and arithmetic stay the calling model's problem. Correct inputs are this server's only problem.

What it is

mise-en-place is not a nutrition database of its own. It's a lookup layer in front of USDA FoodData Central, built so an LLM can search for a food, resolve a quantity ("1 medium onion", "2 tablespoons", "6 oz") to grams, and get back real, sourced macros instead of inventing one. It runs over MCP (stdio), so any MCP-capable client (Claude Code, Claude Desktop, or anything else that speaks the protocol) can connect to it directly.

Eight tools: search_foods, get_nutrition, get_nutrition_batch, save_override, save_recipe, get_recipe, get_recipe_history, list_recipes. Full signatures and schemas are in INTERFACES.md.

Related MCP server: Food Data Central MCP Server

How to use it

Requirements: Python 3.11+, a free USDA FoodData Central API key (instant signup).

git clone <this repo>
cd mise-en-place
uv sync   # or: pip install -e .
export USDA_API_KEY=<your key>

Register it with an MCP client, e.g. Claude Code:

claude mcp add mise-en-place -s local -e USDA_API_KEY=<your key> -- \
  uv run --directory /path/to/mise-en-place mise-en-place

Restart the client so it picks up the new connection, then just ask for what you're cooking. The server's own instructions (visible in server.py's _SERVER_INSTRUCTIONS) travel with the connection and tell the model how to use the tools correctly, so nothing needs to be pasted into the chat by hand. If you're running recurring meal-planning sessions with your own macro targets, see MEAL_PLANNING_PROMPT.md. Targets are per-person and per-meal, so the server never stores them; that one file is where they live.

Personal corrections and saved recipes live in ~/.mise-en-place/ (MISE_HOME), never committed. masses.toml at the repo root is the one shipped, checked-in default table of common household-measure weights (a medium onion, a large egg): human-written, cited, and safe to disagree with by editing it.

Why use it

A lot of "get me the calories" tools answer a question they don't actually know the answer to. This one doesn't:

• Never invents a nutrient value. If USDA doesn't have the number, the tool raises or reports it as explicitly absent, never a zero and never an estimate standing in for a fact.

• Never falls back to a round number. A quantity that can't be resolved to a mass (through USDA's own portion data, your own saved corrections, or the shipped masses.toml defaults) raises instead of defaulting to 100g or guessing a density.

• Every uncertain value says so, out loud. A low-confidence match, a candidate the tool wasn't sure about, an assumed mass: all of it rides back to the calling model as a structured caveat, not a number that looks exactly as confident as a verified one.

• Corrections make the tool better, permanently. save_override and save_recipe mean a mistake gets fixed once, not re-guessed on every future lookup of the same ingredient.

• Measurably more accurate than a model guessing on its own. A 10-recipe benchmark against hand-verified label data put this tool's mean error at 4.99%, against 8.26% for the same model with no tool access. Methodology and the current number are below.

How we test that this is actually true

The rule "never invent a value" is easy to claim and easy to quietly violate. Two things back it up.

Live, re-verified test data. Every USDA ID this project cites, in masses.toml and in the test suite's golden values, is re-fetched from the real API on every run and checked against what it's claimed to be (tests/test_golden_values.py, tests/test_masses_toml.py). A citation that's gone stale fails loudly instead of sitting there looking authoritative.

Accuracy validated against real product data, not another recipe site's opinion. An early accuracy pass compared this tool's output against a popular health-recipe site's published nutrition figures and found the tool diverging by a lot on some recipes. A closer look showed the divergence ran the other way: hand-checking the worst cases against actual product nutrition labels showed this tool's numbers were the ones close to reality, and the third-party site's were the outlier. Comparing against any recipe site turned out to be the wrong test, since its own methodology is opaque and there's no way to know what product it priced, what serving-yield convention it used, or what nutrition database sits behind it.

The real accuracy benchmark this points toward (tests/golden_recipes/) is built from two sources deliberately kept apart: USDA data for genuine commodity ingredients (a potato, granulated sugar: nothing with a brand to speak of), and real product nutrition labels for anything a specific commercial product plausibly means (a brand of tortilla, a packaged tofu). Using USDA data to validate a USDA-backed tool's generic matches would just be checking whether it agrees with itself; label data is the only independent check that actually means something for branded-shaped ingredients. Several of the recipes deliberately carry the same ingredient phrased two ways: a generic name ("tortilla chips") and a specific one ("Mission tortilla chips"), so the benchmark checks both the tool's default generic match and whether it correctly finds and prefers a named product when the recipe asks for one by name.

Latest result (10 recipes, tool-equipped agent vs. bare-LLM-recall agent, both scored against the same hand-verified label data, 2026-08-21): the tool-equipped agent's mean absolute error across calories/protein/ carbs/fat was 4.99%, against 8.26% for the same agent with no tool access. The tool wins outright on 7 of 10 recipes, decisively on recipes with a genuinely hard branded ingredient or a meat cut whose fat content is easy to misremember (chicken thigh, skirt steak). The three recipes where bare recall did as well or better aren't evidence the tool's own matching is weak. They're cases where the recipe itself left a quantity uncountable (a garnish with no stated weight), forcing both conditions to independently guess and adding noise unrelated to what the benchmark is trying to measure. See note_on_TC10 in tests/golden_recipes/eval_prompts.json for the one case worth fixing before trusting its score. This number will drift as the fixture and the tool both keep changing; treat it as the most recent reading, not a permanent claim.

Where to read more