tabedata-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@tabedata-mcp How many calories are in a Big Mac in Japan?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
tabedata-mcp
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.
Who this is for
Tracking macros, sodium, or carbs on a Japanese diet
Looking up allergens on a menu you can't read
Travelers using an AI assistant to navigate Japanese restaurants and konbini
Comparing options across chains ("which chain has the leanest chicken?")
Building nutrition or meal apps that need real Japanese product data
Related MCP server: Wellness Nourish
Install
npm install -g tabedata-mcpOr run on demand with npx -y tabedata-mcp.
Configuration
No API keys needed. Curated database ships with the package.
Variable | Required | Description |
| no |
|
| http only | Bearer token. HTTP transport refuses to start without it. |
| no | Default |
| no | Default |
| no | Comma-separated CORS allowlist for HTTP transport. |
Claude Desktop
Edit claude_desktop_config.json:
{
"mcpServers": {
"tabedata": {
"command": "npx",
"args": ["-y", "tabedata-mcp"]
}
}
}Claude Code
claude mcp add tabedata -- npx -y tabedata-mcpCursor / Windsurf
Add to ~/.cursor/mcp.json with the same shape as Claude Desktop.
Tools
Tool | Description |
| Bilingual fuzzy search across the dataset. Accepts JP or EN queries (e.g. |
| Chain-scoped lookup for 7-Eleven, Lawson, FamilyMart, Ministop. Includes allergens and ingredient lists where available. |
| Chain meal lookup with size variants (並 / 大盛 / 特盛) and allergen tags. |
| Natural-language meal analyzer. Recognizes counts, weights ( |
| 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. |
| Mifflin-St Jeor BMR x activity multiplier x goal-driven deficit/surplus. Diabetes risk shifts the macro split. Hypertension surfaces a sodium guidance note. |
Prompts
Prompt | What it does |
| Given a goal (e.g. "cut to 70kg, hit 150g protein"), chains |
Coverage
Konbini (122 items)
Chain | Items |
7-Eleven | 46 |
Lawson | 34 |
FamilyMart | 29 |
Ministop | 13 |
Restaurant chains (272 items)
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.
Generic, drink, and brand items (66 items)
42 items from Japan's MEXT Standard Tables of Food Composition (文部科学省 食品成分表) — 36 generic staples (rice, egg, natto, tofu…) and 6 basic drinks (milk, tea, coffee). MEXT is the canonical reference used in Japanese nutrition research and clinical practice.
24 brand products: Oikos, SAVAS Milk Protein line, Meiji R-1 / Bulgaria / LG21 / TANPACT, Glico, Calbee, Ito En, Asahi, Kirin, Suntory, Fuji Pan, Snow Brand, Morinaga inZeri.
Example queries
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, cuttingData provenance
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:
A cited source — an official manufacturer or restaurant nutrition label, an official PDF, an entry from Japan's MEXT Standard Tables of Food Composition, or a flagged estimate (
source.type: "estimated") where official figures weren't available.Bilingual names —
name_enandname_jpare mandatory.Provenance fields —
source.url,source.type,compiled_at(ISO date),compiled_by, and aconfidencelevel.
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.
Disclaimer
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.
Contributing
See CONTRIBUTING.md. Pull requests for new items welcome — include source.url, source.type, bilingual names, and a confidence level for each.
License
Available Tools
6 toolsanalyze_mealAnalyze MealARead-onlyIdempotent
PREFERRED tool whenever the user describes a whole meal (two or more items): resolves every item in ONE call and returns macro totals plus target comparison. Do NOT decompose a meal into separate konbini_item/restaurant_meal/search_food lookups. In clients that support MCP Apps it also renders a visual nutrition label. Recognizes counts ('2 eggs'), weights ('200g rice'), fractions ('half avocado', '3/4 portion'), and restaurant size names ('Nakau large oyakodon'). Optional user_profile compares totals against personalized targets (pass it whenever the user shares their stats).
自然言語の食事リストを解析し、PFC・繊維・ナトリウムの合計を返します。「2個」「200g」「半分」「3/4」「Nakau 大盛」など多様な指定に対応。user_profile を渡すと個別目標との差分も返します。
Inputs: items (required), user_profile.
daily_targetsCalculate Personalized Daily TargetsARead-onlyIdempotent
Calculate personalized daily calorie, protein, carb, fat, fiber, and water targets. Uses Mifflin-St Jeor for BMR, an activity multiplier for TDEE, and a goal-driven deficit/surplus. Diabetes risk shifts the macro split toward fat (35%) to lower carb load. Hypertension surfaces a sodium guidance note.
個別の1日あたりカロリー・PFC・繊維・水分量を算出します。BMR は Mifflin-St Jeor、TDEE は活動係数、目標から赤字・黒字を決定。糖尿病リスクがある場合は脂質比率を高めて糖質負荷を抑えます。
Inputs: age (required), sex (required), goal (required), activity (required), +3 more.
find_alternativesFind Better Food AlternativesARead-onlyIdempotent
Find alternatives for a food along a chosen axis: higher protein, lower calorie, or lower sodium. Returns each alternative with its improvement and tradeoff.
ある食品の代替案を、たんぱく質・カロリー・ナトリウムなど指定軸で提案します。改善点とトレードオフを併記します。
Inputs: current_food (required), optimize_for (required), constraint, max_results.
konbini_itemKonbini Item LookupARead-onlyIdempotent
Look up konbini items (7-Eleven, Lawson, FamilyMart, Ministop) by chain. Most items are AI-compiled from official product labels; a small share are flagged estimates where label data was unavailable — each item returns its source URL, type, and confidence so you can check the value. Values are not individually human-verified; treat them as a reference.
コンビニ商品(セブン-イレブン・ローソン・ファミリーマート・ミニストップ)をチェーン別に検索します。多くは公式の商品表示からAIが編集していますが、一部は表示が入手できず推定値です。各商品に出典URL・種別・信頼度を付与しているため値を確認できます。人手による個別検証は行っていないため参考値としてご利用ください。
Inputs: chain (required), search, category.
restaurant_mealRestaurant Chain Meal LookupARead-onlyIdempotent
Look up a restaurant chain meal with size variants, AI-compiled from official chain nutrition PDFs, allergen tables, and product labels — cited per item, with a small share flagged as estimates where official figures were unavailable. Values are not individually human-verified; treat them as a reference. Default size is 並 (regular); the modifications field is recorded but not yet applied to nutrition totals.
飲食チェーンのメニューをサイズ違いで検索します。各社公式の栄養PDF・アレルゲン表・商品表示をもとにAIが編集し、商品ごとに引用URLを保持(一部は公式値が入手できず推定値)。人手による個別検証は行っていないため参考値としてご利用ください。既定サイズは並。「玉子追加」などの変更項目は記録のみで、栄養への反映は未対応です。
Inputs: item (required), chain (required), size, modifications.
search_foodSearch Japanese Food DatabaseARead-onlyIdempotent
Search an AI-compiled 460-item Japanese food database by English or Japanese keyword (e.g. 'salad chicken' or 'サラダチキン'), returning macros, allergens, and a cited source per item. Sources are manufacturer labels, restaurant nutrition PDFs, Japan's MEXT food-composition database, or flagged estimates; each item carries a confidence level and a source URL to check the value against. Values are AI-compiled from those sources, not individually human-verified — treat them as a reference.
AIが編集した日本の食品データベース(460件)を日本語・英語のキーワードで検索し、各商品のマクロ栄養素・アレルゲン・出典を返します。出典はメーカー表示・飲食店の栄養PDF・文部科学省食品成分データベース・推定値のいずれかで、各商品に信頼度と確認用の出典URLが付きます。値はAIが各出典から編集したもので、人手による個別検証は行っていません。参考値としてご利用ください。
Inputs: query (required), category, language, max_results.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose: general food search, convenience store items, restaurant meals, whole meal analysis, alternative suggestions, and daily target calculation. No overlap exists.
All tool names follow a consistent snake_case pattern with verb_noun or noun_noun structure, making them predictable and easy to distinguish.
Six tools is well-scoped for the server's purpose of Japanese food lookup and analysis. Each tool addresses a distinct need without being overwhelming.
The tool set covers core use cases: searching foods, analyzing meals, finding alternatives, and setting daily targets. Minor gaps exist (e.g., no ability to add custom foods or track history), but the set is sufficient for reference and planning.
Maintenance
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