
Tabedata Mcp
io.github.mrslbtPublished by GitHub account mrslbtv1.5.0Updated Oct 24, 2026
Bilingual JP/EN Japanese food nutrition for AI: 460 sourced konbini, chain & brand items.
Overview
A local MCP server giving assistants bilingual JP/EN nutrition data for 460 Japanese konbini, restaurant chain and brand food items.
- What you can do
- Search the dataset with fuzzy JP or EN queries and look up konbini items by chain, including allergens and ingredients where available. Look up restaurant chain meals with size variants and allergen tags. Analyze a described meal, recognizing counts, weights, fractions and size names, and return macro totals against optional personal targets. Suggest alternatives along an axis such as higher protein, lower calorie or lower sodium, with tradeoffs. Calculate daily calorie and macro targets from body data, activity and goal.
- When to use it
- Useful when tracking macros, sodium or carbs on a Japanese diet, checking allergens on a menu you cannot read, comparing items across konbini and restaurant chains, or building nutrition apps that need Japanese product data.
- Requirements
- Runs locally over stdio via the npm package tabedata-mcp, installed globally or run with npx. No API keys are needed; the curated database ships with the package. Optional HTTP transport uses MCP_TRANSPORT, MCP_AUTH_TOKEN, MCP_HTTP_PORT, MCP_HTTP_HOST and MCP_HTTP_ALLOWED_ORIGINS.
Installation
In SourceWeft
- Open Tabedata Mcp in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
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
Or run on demand with npx -y tabedata-mcp.
Configuration
No API keys needed. Curated database ships with the package.
Claude Desktop
Edit claude_desktop_config.json:
Claude Code
Cursor / Windsurf
Add to ~/.cursor/mcp.json with the same shape as Claude Desktop.
Tools
Prompts
Coverage
Konbini (122 items)
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
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_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
Source: README.md at commit 7e82d83
Tools
0Version history
1- v1.5.0LatestSep 16, 2026
Score
50% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 days25 pts
Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.
- Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts