# Gurume CLI

> Use the `gurume` CLI to search Japanese restaurants on Tabelog. Trigger this whenever the user wants to find, recommend, or research restaurants/eateries in Japan — by area (Tokyo, Osaka, Kyoto, 三重, etc.), by cuisine (ramen, sushi, yakiniku, izakaya...), or with a vague request like "where should I eat in Shibuya" or "any good unagi spots near Tokyo Station". Use this even when the user does not explicitly mention Tabelog or the `gurume` tool, as long as the intent is finding Japanese restaurants.

- Skill: `narumiruna/gurume-cli` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add narumiruna/gurume-cli`
- Raw SKILL.md: https://api.skillmd.com/api/skills/narumiruna/gurume-cli/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: narumiruna (https://skillmd.com/u/narumiruna)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/narumiruna/gurume-cli

---


# gurume CLI

`gurume` is a CLI wrapper around Tabelog (Japan's largest restaurant review site). Use it instead of guessing restaurants from memory or scraping the web — its data is current and structured.

## When to use

Trigger this skill whenever the user wants to find restaurants in Japan. Typical signals:

- Mentions a Japanese place name (Tokyo, 大阪, Shibuya, 三重, 京都駅, ...)
- Mentions Japanese cuisines (ramen, sushi, unagi, yakiniku, izakaya, 寿司, ラーメン, ...)
- Asks for restaurant recommendations / "where to eat" in Japan
- Plans a trip to Japan and asks about food

Do **not** trigger for: non-Japan restaurants, recipes, food delivery apps, or general food trivia.

## Workflow

### 1. Resolve the cuisine (if any)

If the user mentions a specific cuisine, map it to one of Tabelog's supported cuisine names. The full list is in `references/cuisines.md` — read that file when you need the mapping. Cuisine names **must be passed in Japanese** (e.g. `ラーメン`, not `ramen`).

If the user's request doesn't fit the fixed list (e.g. "tonkotsu ramen", "omakase sushi", "okonomiyaki"), drop
`--cuisine` and put the extra detail into `--keyword` instead. Cuisine + keyword can be combined in the CLI, but
the MCP tool intentionally rejects `keyword + cuisine`; prefer the shared behavior of either supported cuisine-only
search or best-effort keyword search.

### 2. Resolve the area

Pass the area in Japanese where possible (`東京`, `大阪`, `渋谷`, `京都`, `三重`). Romaji sometimes works, but Japanese is more reliable. Use the most specific area the user gave you — `渋谷` returns better results than `東京` if the user said "Shibuya".

### 3. Run the search

Default invocation — use JSON output so you can parse results cleanly:

```bash
gurume search --area <area> [--cuisine <jp-cuisine>] [--keyword <jp-keyword>] \
              --sort ranking --limit 10 --output json
```

Flag guidance:

- `--sort ranking` (default): good general "best of" results. Use `review-count` when the user wants popular/famous places, `new-open` for newly opened spots.
- `--limit`: 10 is plenty for a conversational reply. Bump to 20+ only if the user asks for a long list.
- `--output json`: preferred for agents. It returns `status`, `items`, `meta`, `applied_filters`, `warnings`, and structured `error` fields. Use legacy `--output json-list` only if you specifically need the old list-only shape.

### 4. Present results

After running, parse the JSON envelope. If `status` is `error`, surface `error.message` and `error.suggested_action`
instead of inventing restaurants. Otherwise summarize `items` for the user. For each restaurant include:

- Name (keep the Japanese name; add a romaji/English hint only if it helps)
- Cuisine / area
- Rating and review count if present
- A one-line note (price range, station, what stands out)
- The Tabelog URL so they can click through

When the envelope includes `warnings`, account for them in your answer. When the command used both `--area` and
`--keyword`, check `meta.area_filter_confidence` first. Treat `low`, missing, or unavailable confidence as low
confidence unless the result URLs prove the requested area. Do not present broad keyword results as clean area-scoped
recommendations. If the user asked for Osaka and the JSON includes non-`/osaka/` URLs, say that the CLI returned
mixed-area results and either keep only the clearly Osaka URLs or ask whether they want a broader keyword list. This
mirrors the MCP tool's warning that keyword searches may need suggestion validation and cuisine-specific filtering. If
MCP suggestions return a cuisine-like value that is not in `gurume list-cuisines`, keep using keyword search.

Then ask if they want to narrow down (different area, cheaper, dinner only, etc.).

## Examples

**User:** "Looking for good ramen in Tokyo, ideally tonkotsu."

```bash
gurume search --area 東京 --cuisine ラーメン --keyword 豚骨 --sort ranking --limit 10 --output json
```

**User:** "Any famous sushi places in Osaka?"

```bash
gurume search --area 大阪 --cuisine 寿司 --sort review-count --limit 10 --output json
```

**User:** "Where should I eat dinner near Kyoto Station?" (no specific cuisine)

```bash
gurume search --area 京都駅 --sort ranking --limit 10 --output json
```

**User:** "I want sukiyaki in Mie prefecture."

```bash
gurume search --area 三重 --cuisine すき焼き --sort ranking --limit 10 --output json
```

## Other commands (rarely needed)

- `gurume list-cuisines` — print the supported cuisine list. You usually don't need to run this because `references/cuisines.md` already has it cached; only re-run if the user suspects the list is outdated.
- `gurume tui` / `gurume mcp` — interactive TUI and MCP server. Don't invoke these from this skill; they're for the user to run themselves.

## Notes & gotchas

- Tabelog HTML is the upstream source, so results occasionally contain odd encoding or missing fields. Handle missing keys gracefully when summarizing.
- The CLI hits the network — if it fails, surface the error to the user rather than fabricating restaurants.
- `--area` + `--keyword` can be broad even when `applied` text looks area-specific. Use URL evidence before claiming
  a result belongs to the requested area.
- Reply to the user in the same language they used (often Traditional Chinese, Japanese, or English).