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Tabedata MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/7/2026, 10:53:23 PM

Tabedata MCP

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time — check back soon.
View Repository2 GitHub StarsTotal stargazers on GitHub for the source repository (2 stars).

Bilingual JP/EN Japanese food nutrition for AI: 460 sourced konbini, chain & brand items.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "tabedata-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "tabedata-mcp"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives💻 More in Developer Tools

Documentation Overview

tabedata-mcp

CI npm version npm downloads License: MIT

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

Install

Terminal
npm install -g tabedata-mcp

Or run on demand with npx -y tabedata-mcp.

Configuration

No API keys needed. Curated database ships with the package.

VariableRequiredDescription
MCP_TRANSPORTnostdio (default) or http.
MCP_AUTH_TOKENhttp onlyBearer token. HTTP transport refuses to start without it.
MCP_HTTP_PORTnoDefault 8787.
MCP_HTTP_HOSTnoDefault 127.0.0.1.
MCP_HTTP_ALLOWED_ORIGINSnoComma-separated CORS allowlist for HTTP transport.

Claude Desktop

Edit claude_desktop_config.json:

config.json
{
  "mcpServers": {
    "tabedata": {
      "command": "npx",
      "args": ["-y", "tabedata-mcp"]
    }
  }
}

Claude Code

Terminal
claude mcp add tabedata -- npx -y tabedata-mcp

Cursor / Windsurf

Add to ~/.cursor/mcp.json with the same shape as Claude Desktop.

Tools

ToolDescription
search_foodBilingual fuzzy search across the dataset. Accepts JP or EN queries (e.g. salad chicken or サラダチキン).
konbini_itemChain-scoped lookup for 7-Eleven, Lawson, FamilyMart, Ministop. Includes allergens and ingredient lists where available.
restaurant_mealChain meal lookup with size variants (並 / 大盛 / 特盛) and allergen tags.
analyze_mealNatural-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_alternativesSwap 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_targetsMifflin-St Jeor BMR x activity multiplier x goal-driven deficit/surplus. Diabetes risk shifts the macro split. Hypertension surfaces a sodium guidance note.

Prompts

PromptWhat it does
plan_my_dayGiven a goal (e.g. "cut to 70kg, hit 150g protein"), chains daily_targets → meal proposal → analyze_meal → find_alternatives into one guided workflow.

Coverage

Konbini (122 items)

ChainItems
7-Eleven46
Lawson34
FamilyMart29
Ministop13

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

Code
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

Data 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:

  1. 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.
  2. Bilingual names — name_en and name_jp are mandatory.
  3. Provenance fields — 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.

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

MIT

Read the full README →View source on GitHub →

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Frequently Asked Questions about Tabedata MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "tabedata-mcp": { "command": "npx", "args": ["-y", "tabedata-mcp"] } }

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Technical Specs & Signals

Category💻Developer Tools
More technical detailsExpand ▾
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
Views0
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Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars2
GitHub Star CountTotal stargazers on GitHub representing community popularity (2 stars).
37Quality signal: Fair · 37/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools16/30
Adoption & activity2/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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