Tabedata MCP

Name: Tabedata MCP
Author: mrslbt

by mrslbtversion 1.5.0i★ –iRuns on your computer

Registry nameio.github.mrslbt/tabedata-mcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiVery new↓ 64/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Clinics & health

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "tabedata": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "tabedata-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account mrslbt, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Clinics & healthi

More stars than 0% of the other 1027 servers in this category
Published to the registry more recently than 26% of the other 1027 servers in this category
Basic checks equal to or better than 100% of the other 1027 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Tabedata MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.5.0) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Mirobody, FHIR MCP Server, Google Health MCP

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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Install

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.

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.

Claude Desktop

Edit claude_desktop_config.json:

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

Claude Code

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

Cursor / Windsurf

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

Tools

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.

Prompts

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.

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, 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:

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_en and name_jp are mandatory.
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

Tabedata MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Clinics & healthi

Common questions

Alternatives

Mirobody

FHIR MCP Server

Google Health MCP

README from the maker

tabedata-mcp

Who this is for

Install

Configuration

Claude Desktop

Claude Code

Cursor / Windsurf

Tools

Prompts

Coverage

Konbini (122 items)

Restaurant chains (272 items)

Generic, drink, and brand items (66 items)

Example queries

Data provenance

Disclaimer

Contributing

License