TACO Brasil

taco-br-mcp

MCP server that lets an AI assistant answer with exact numbers from the TACO — Tabela Brasileira de Composição de Alimentos, 4ª ed. (NEPA/UNICAMP, 2011) — instead of guessing. 597 Brazilian foods, 26 nutrients, per 100 g of edible portion, plus household measures ("2 colheres de sopa", "1 unidade") from the IBGE POF.

Em português: servidor MCP com a Tabela Brasileira de Composição de Alimentos (TACO). Busca alimentos, devolve nutrientes, soma refeições e converte medidas caseiras em gramas. Toda resposta cita a fonte, e o que a tabela não traz vem como "sem dado", nunca como zero.

stdio by default, for Claude Desktop, Claude Code, Cursor and other local MCP clients. It also serves Streamable HTTP (--http), which is how ChatGPT connects: see Remote HTTP and ChatGPT.

Install

Requires Node.js 20 or newer.

Claude Code

claude mcp add taco-br -- npx -y taco-br-mcp

Claude Desktop (claude_desktop_config.json) and Cursor (.cursor/mcp.json)

{  "mcpServers": {    "taco-br": { "command": "npx", "args": ["-y", "taco-br-mcp"] }  }}

Then ask, for example: "Quantas kcal tem uma refeição de 4 colheres de sopa de arroz, 100 g de feijão carioca e 100 g de peito de frango grelhado?"

Remote HTTP and ChatGPT

ChatGPT cannot reach a server running on your PC, so the same tools are available over Streamable HTTP:

npx -y taco-br-mcp --http   # http://127.0.0.1:3000/mcp

A public instance runs at https://taco-br-mcp.taco-br-mcp.workers.dev/mcp (open, no login, rate limited; best effort, no uptime promise). The repository also ships a Cloudflare Workers deployment (free plan, open access, per-IP rate limit). Step-by-step deploy and the ChatGPT connector setup (Settings → Apps & Connectors, developer mode, authentication "No authentication") are in docs/remote.md.

Tools

All tools are read-only. Every response cites TACO 4ª ed. (NEPA/UNICAMP, 2011).

How it treats missing data

• Tr (trace, below the quantification limit) is {"trace": true}, never 0.
• A value the table does not publish is null and shown as "sem dado na TACO", never 0. The source marks these cells in three ways (NA, * for "analyses under re-evaluation", or blank); the processed CSV merges them, so the server does not tell them apart. Whole milk, for example, has no published energy or protein.
• Fatty and amino acids come from separate TACO tables that cover only part of the foods (423 and 26 of 597). A food outside the table gets available: false, never a row of zeros. In the amino acid table the TACO prints 0,00 for amounts under 0.005 g/100 g; that is a published value (shown as 0 or a small number), not missing data.
• calc_meal totals are lower bounds when an item lacks data for a nutrient: the response says which foods are missing.
• Household weights are POF/IBGE averages, not your plate. Only foods and measures whose POF description matches the food were mapped; anything else is reported as not covered.

Mini-evaluation

15 questions about the TACO, answered by claude -p with and without the server, 3 repetitions each (90 runs per model, 30 October 2026), on three models. A numeric answer is correct when it is within 5% of the table value; for the two "honesty" questions it is correct only if it says the TACO does not publish the value (whole milk energy) or that the total is a minimum (fiber of a meal that includes chicken, which has no fiber value).

Reading: with the server all three models got 45/45. Without it they score 31 to 39 out of 45, and the failures differ by model. Haiku gives remembered values that are wrong for single foods (vitamin C in banana prata, iron in cooked lentils), labels them as unverified, and does not flag the fiber total of the meal as a minimum. Sonnet mostly answers "no data" on household measures and meals. Opus remembers most single values and still guesses unit weights (2 boiled eggs). All three, in all 3 runs, answer 61 kcal for whole milk, a value the TACO does not publish. One side effect: Haiku with the server repeated the TACO citation in only 13 of 45 answers (the tool output always carries it); Sonnet and Opus did it in 45 of 45. Limits: 15 questions, 3 repetitions, rule-based scoring (the 5% tolerance accepted one no-MCP Sonnet answer with a wrong rice value that landed near the right sum). Questions, answer key and raw answers are in eval/ (one folder per model); to repeat: npm run build && node eval/run.mjs --model claude-haiku-5-5 && node eval/score.mjs --all --detail.

Development

npm installnpm run buildnpm testnpm run inspect   # MCP Inspector against dist/server.jsnpm run build:data -- --fetch   # regenerate data/ from the pinned upstream CSVs

Never write to stdout (console.log): it is the protocol channel. Use console.error. See AGENTS.md for the project rules.

Data & license

Code: MIT. Nutritional data: TACO 4ª ed., NEPA/UNICAMP (2011). Household-measure weights: Fonte: IBGE, POF 2008-2009, Tabela de Medidas Referidas para os Alimentos Consumidos no Brasil — the grams are IBGE's, unchanged; the mapping between TACO and POF foods was curated by this project, which is responsible for it. See NOTICE.md for sources, processing and pinned versions. If you use this in research, see CITATION.cff. Not medical or nutritional advice.