Indian Food Nutrition Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Indian Food Nutrition Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Your AI assistant can finally count Indian food calories correctly.
An MCP server that lets you log meals through Claude (and soon ChatGPT) in plain language: "2 rotis and a katori of dal", or just a photo of your plate. Calories and macros come from India's official food composition data (IFCT 2017, National Institute of Nutrition) plus USDA for everything else, not from US-centric databases that think a roti is a tortilla.
Every popular calorie database is built on USDA data. It is inaccurate for home-cooked Indian food: wrong oils, wrong preparations, no katori, no idli. The one app with a great Indian database keeps it locked behind a subscription with no API. Meanwhile the Indian government published the real data. This project wraps it for the AI you already talk to, and gives that AI memory of what you actually ate.
Hyderabad. Ghee, atta, dals, regional varieties.
the model asks instead of guessing portions.
fetch_image).get_history returns your real intake so the model cancoach you ("your protein is low on training days") against data, not vibes.
mirror at ~/.nutrition-mcp/meals.csv. No account, no cloud, no telemetry.
npm install -g indian-food-nutrition-mcpAdd to claude_desktop_config.json (Settings → Developer → Edit Config):
{
"mcpServers": {
"nutrition": {
"command": "indian-food-nutrition-mcp"
}
}
}Restart Claude Desktop, then just talk:
"Log breakfast: 3 idlis and a small bowl of sambar" "How much protein have I had today?" "Here's a photo of my lunch, log it" "Look at my last week and tell me where my diet is failing"
| Tool | What it does |
|---|---|
search_food | Search 8,300+ foods (IFCT + USDA), per-100g cal/protein/carb/fat/fiber |
log_meal | Log items with mandatory qty + household unit; DB-derived macros |
get_day | A day's log + totals |
get_history | Per-day totals over a range, the AI-coaching context block |
edit_entry / delete_entry | Fix mistakes so history stays honest |
fetch_image | Pull a food photo from a URL so the model can see and log it |
Code is AGPL-3.0-or-later. Bundled data: IFCT 2017 + USDA SR Legacy (public domain). The INDB cooked-dish dataset (dal, dosa, idli as dishes with serving sizes) is supported by the code but not redistributed until its authors grant a license; generate it locally for personal use with npm run build:indb. Full provenance: DATA_SOURCES.md.
This local server works with Claude Desktop today. A hosted version, which works as a ChatGPT connector and syncs across devices, is coming. Open an issue titled "hosted" or watch releases to get in early.
~/.nutrition-mcp/nutrition.db (SQLite, WAL) + ~/.nutrition-mcp/meals.csv (auto-maintained mirror). Override with NUTRITION_DB_PATH / NUTRITION_CSV_PATH.
(via the @nodef/ifct2017 package, AGPL)
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.