# io.github.mrslbt/tabedata-mcp (npm · tabedata-mcp)

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

- Trust score: 68/100 (medium)
- Change this week: +18
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- npm · `tabedata-mcp`: 68/100 (this document), [markdown](https://verifymcp.io/servers/mrslbt-tabedata-mcp/tabedata-mcp.md), [page](https://verifymcp.io/servers/mrslbt-tabedata-mcp/tabedata-mcp)

## Channel facts

- Registry: `npm`
- Package: `tabedata-mcp`
- Version: `1.5.0`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Supply Chain Security**: 86/100
  - No malware found by supply-chain analysis.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
  - No install/post-install scripts declared.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 1 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 74/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 2089 tokens (~298/item across 7 items; 6 tools + 1 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 23/100
  - Stability observed for 7 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.
  - Supports UI / widget rendering.

## Install

### Claude

```bash
claude mcp add mrslbt-tabedata-mcp -- npx -y tabedata-mcp
```

### Codex

```bash
codex mcp add mrslbt-tabedata-mcp -- npx -y tabedata-mcp
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "mrslbt-tabedata-mcp": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "tabedata-mcp"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add mrslbt-tabedata-mcp --command npx --arg -y --arg tabedata-mcp
```

### Hermes

```yaml
mcp_servers:
  mrslbt-tabedata-mcp:
    command: "npx"
    args: ["-y", "tabedata-mcp"]
```

### Other

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

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-02 (score 68, +44)

- [security regression] Provenance: unverified → fail
- [security improvement] Install scripts: unverified → pass
- [security improvement] Known CVEs: unverified → partial
- [security improvement] Malware scan: unverified → pass
- [functional regression] Schema quality: 100 → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional improvement] Schema quality: 335 → 298
- [functional improvement] Schema quality: unverified → excellent
- [functional improvement] License: unverified → pass
- [functional improvement] Stability: unverified → 0.20
- [functional improvement] Maintenance: unverified → pass
- [functional improvement] MCP protocol: unverified → pass
- [functional improvement] Dependency health: unverified → partial
- [functional] First check of Capabilities: pass
- [functional] Licence: MIT
- [functional] Package version: 1.2.0 → 1.5.0

### 2026-07-31 (score 24, −8)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 32, −18)

- [security regression] Malware scan: pass → unverified

### 2026-07-27 (score 50)

First indexed and scored.

## MCP tools (6)

### `search_food` (~374 tokens)

Search Japanese Food Database

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。

Input parameters:

- `category` (string): Restrict to a category. Omit to search all. カテゴリで絞り込み。
- `language` (string): Match against EN names, JP names, or both. Defaults to both. 検索対象の言語。
- `max_results` (integer): Maximum number of results (1–50, default 10). 最大結果数。
- `query` (string, required): Search term (English or Japanese), e.g. 'salad chicken' or 'サラダチキン'. 検索語(英語または日本語)。

### `konbini_item` (~266 tokens)

Konbini Item Lookup

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。

Input parameters:

- `category` (string): Subcategory filter. Defaults to any. サブカテゴリ。
- `chain` (string, required): Konbini chain. コンビニチェーン。
- `search` (string): Optional search within the chain (EN or JP). Omit to list the whole chain. 店内検索(英語または日本語)。

### `restaurant_meal` (~406 tokens)

Restaurant Chain Meal Lookup

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。

Input parameters:

- `chain` (string, required): Restaurant chain slug. Live for 17 chains; saizeriya, pepper-lunch, kichiri-misshoku, starbucks-jp, and burger-king-jp are coming soon and currently return no items. 飲食チェーン。17チェーンが利用可能。サイゼリヤ・ペッパーランチ・…
- `item` (string, required): Menu item to look up (EN or JP), e.g. 'gyudon' or '牛丼'. 検索するメニュー名。
- `modifications` (array): Modifications such as 'no rice' or '玉子追加'. Recorded but not yet applied to nutrition totals. メニューの変更指示。
- `size` (string): Size variant. Defaults to regular (並). サイズ。既定値: regular(並)。

### `analyze_meal` (~274 tokens)

Analyze Meal

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。

Input parameters:

- `items` (array, required): Free-form meal items (EN or JP). Examples: ['1 oikos plain', '200g rice', 'Nakau large oyakodon, 3/4 rice']. 食事項目。
- `user_profile` (object): Optional user profile. When present, totals are compared against personalized targets. ユーザー情報。

### `find_alternatives` (~204 tokens)

Find Better Food Alternatives

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。

Input parameters:

- `constraint` (string): Restrict alternatives to the same chain, same category, or any. Defaults to any. 制約条件。
- `current_food` (string, required): Current food (EN or JP), e.g. 'tuna mayo onigiri' or 'ツナマヨおにぎり'. 改善したい現在の食品。
- `max_results` (integer): Maximum alternatives to return (1–10, default 3). 最大件数。
- `optimize_for` (string, required): Which axis to improve along. 改善したい指標。

### `daily_targets` (~312 tokens)

Calculate Personalized Daily Targets

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。

Input parameters:

- `activity` (string, required): Activity level. 活動レベル。
- `age` (integer, required): Age in years, e.g. 30. 年齢。
- `conditions` (array): Optional health conditions. Adjusts macro split (diabetes_risk shifts toward fat) and surfaces guidance notes (hypertension flags sodium ceiling). 持病。
- `goal` (string, required): Body composition goal. 目標。
- `height_cm` (number, required): Height in centimeters, e.g. 175. 身長(cm)。
- `sex` (string, required): Biological sex (used by the BMR formula). 性別。
- `weight_kg` (number, required): Body weight in kilograms, e.g. 70. 体重(kg)。

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/mrslbt-tabedata-mcp/tabedata-mcp#diagnostics

## Score history

- 2026-08-03: 68
- 2026-08-02: 68
- 2026-08-01: 24
- 2026-07-31: 24
- 2026-07-30: 32
- 2026-07-28: 50
- 2026-07-27: 50

## Links

- npm package: https://www.npmjs.com/package/tabedata-mcp
- Socket report: https://socket.dev/npm/package/tabedata-mcp
- Repository: https://github.com/mrslbt/tabedata-mcp
- Changelog RSS feed: https://verifymcp.io/servers/mrslbt-tabedata-mcp/tabedata-mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/mrslbt-tabedata-mcp/tabedata-mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/mrslbt-tabedata-mcp/tabedata-mcp