Bilingual JP/EN Japanese food nutrition for AI: 460 sourced konbini, chain & brand items.
Bilingual JP/EN Japanese food nutrition for AI: 460 sourced konbini, chain & brand items.
Valid MCP server (2 strong, 1 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry. Trust signals: trusted author (4/4 approved).
8 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-mrslbt-tabedata-mcp": {
"args": [
"-y",
"tabedata-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
Model Context Protocol server for Japanese food nutrition data. Bilingual JP/EN lookups across konbini, restaurant chains, and grocery brands. Macros, allergens, sodium, and menu navigation for any AI assistant operating in Japan.
460 sourced items across 21 chains. 42 come from Japan's MEXT food composition database (36 generic staples + 6 drinks); the rest are transcribed from official manufacturer labels and restaurant nutrition PDFs, with ~10% flagged as estimates where official figures weren't available. Size variants (並 / 大盛 / 特盛) on every restaurant chain that publishes them.
npm install -g tabedata-mcp
Or run on demand with npx -y tabedata-mcp.
No API keys needed. Curated database ships with the package.
| Variable | Required | Description |
|---|---|---|
MCP_TRANSPORT | no | stdio (default) or http. |
MCP_AUTH_TOKEN | http only | Bearer token. HTTP transport refuses to start without it. |
MCP_HTTP_PORT | no | Default 8787. |
MCP_HTTP_HOST | no | Default 127.0.0.1. |
MCP_HTTP_ALLOWED_ORIGINS | no | Comma-separated CORS allowlist for HTTP transport. |
Edit claude_desktop_config.json:
{
"mcpServers": {
"tabedata": {
"command": "npx",
"args": ["-y", "tabedata-mcp"]
}
}
}
claude mcp add tabedata -- npx -y tabedata-mcp
Add to ~/.cursor/mcp.json with the same shape as Claude Desktop.
| Tool | Description |
|---|---|
search_food | Bilingual fuzzy search across the dataset. Accepts JP or EN queries (e.g. salad chicken or サラダチキン). |
konbini_item | Chain-scoped lookup for 7-Eleven, Lawson, FamilyMart, Ministop. Includes allergens and ingredient lists where available. |
restaurant_meal | Chain meal lookup with size variants (並 / 大盛 / 特盛) and allergen tags. |
analyze_meal | Natural-language meal analyzer. Recognizes counts, weights (200g rice), fractions (half avocado), and restaurant size names (Nakau large oyakodon). Returns macro totals plus optional comparison to personalized targets. |
find_alternatives | Swap an item for a better one along a chosen axis (higher protein, lower calorie, lower sodium). Returns each alternative with its improvement and tradeoff. |
daily_targets | Mifflin-St Jeor BMR x activity multiplier x goal-driven deficit/surplus. Diabetes risk shifts the macro split. Hypertension surfaces a sodium guidance note. |
| Prompt | What it does |
|---|---|
plan_my_day | Given a goal (e.g. "cut to 70kg, hit 150g protein"), chains daily_targets → meal proposal → analyze_meal → find_alternatives into one guided workflow. |
| Chain | Items |
|---|---|
| 7-Eleven | 46 |
| Lawson | 34 |
| FamilyMart | 29 |
| Ministop | 13 |
Japanese chains: Nakau, Sukiya, Yoshinoya, Matsuya, CoCo Ichibanya, Marugame Seimen, Tenya, MOS Burger, Yayoiken, Ootoya, Ichiran.
Western and global chains: McDonald's Japan, KFC Japan, Subway Japan, Lotteria, Freshness Burger, Doutor.
How many calories are in a Big Mac in Japan?
日本のビッグマックは何キロカロリー?
I just had a Sukiya gyudon. Track it.
今日すき家の牛丼食べた。記録して
Compare a 7-Eleven salmon onigiri to a Lawson one
セブンとローソンの鮭おにぎり、どっちがいい?
Analyze my lunch: 1 oikos plain, 2 boiled eggs, 200g rice
ランチ記録して: オイコス1個、ゆで卵2個、白米200g
What sizes does Sukiya's gyudon come in?
すき家の牛丼のサイズ展開は?
Calculate daily targets: 80kg, 175cm, 30, male, moderate, cutting
This dataset is AI-compiled: each value was gathered by an AI assistant (Claude) reading the cited source, then schema-checked by script. It is not individually fact-checked by a human. Treat every number as a best-effort reference and confirm against the item's source.url before relying on it.
Each item carries:
source.type: "estimated") where official figures weren't available.name_en and name_jp are mandatory.source.url, source.type, compiled_at (ISO date), compiled_by, and a confidence level.Run npm run verify-data to validate structure: required fields, id uniqueness, and that each source.url still resolves. This confirms links are live and records are well-formed — it does not verify the correctness of the nutrition numbers. Items older than 365 days are flagged stale.
See DATA_SOURCES.md for the full compilation methodology.
This is an unofficial, community-built MCP server. Not affiliated with, endorsed by, or sponsored by any of the listed restaurants, konbini chains, or product manufacturers; their names and trademarks belong to their respective owners. Nutrition values were AI-compiled by an assistant reading publicly published sources (manufacturer, restaurant, and MEXT government pages) and were not individually verified by a human. Published figures change and AI compilation can introduce errors, so treat every value as a reference, not a guarantee, and confirm against the official source before acting on it. The author accepts no liability for decisions made on the basis of this data.
See CONTRIBUTING.md. Pull requests for new items welcome — include source.url, source.type, bilingual names, and a confidence level for each.
Be the first to review this server!
by Modelcontextprotocol · Developer Tools
Read, search, and manipulate Git repositories programmatically
by Modelcontextprotocol · Developer Tools
Web content fetching and conversion for efficient LLM usage
by Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.