Restaurant market researchers
Area snapshots
Favorable to scraping
Build a compact list of restaurants for a ward, station area, or cuisine niche, then review names and source URLs before deeper manual qualification.
Restaurant Directory$50Free
Tabelog Store List Scraper
The Tabelog store list scraper turns selected Tabelog restaurant detail pages into a tidy CSV for restaurant research. Import the workflow, add the Tabelog URLs you want to review, and export area-style headings, restaurant names, and canonical restaurant URLs from the local desktop app without writing browser automation code.
Output
CSV
Columns
3
Input mode
Multi-URL
Pacing
Load + sleep
Template
Free import
At a glance
Export Tabelog restaurants to CSV
Restaurant detail pages become spreadsheet rows
Each run creates a structured Tabelog to CSV export with one row per detail page. The stock schema focuses on compact directory fields: a location-based heading, the restaurant name, and the canonical URL.
Multi-URL input is built in
The Navigate block includes sample Tokyo restaurant URLs. Replace or expand that list to collect the store records that match your city, cuisine, or competitor set.
Exports stay on your machine
The template runs inside the UScraper local desktop app and writes to your configured save folder. There is no hosted scraper queue in the default workflow.
Built around the available page structure
No listing-page pagination was detected in the supplied analysis, so this template is intentionally detail-page driven. Use it when you already have restaurant URLs or can prepare them from another allowed source.
Who this is for
Practical Tabelog restaurant data use cases
Hospitality operators
Competitive checks
Favorable to scraping
Export Tabelog restaurants for competitor tracking, location scouting, or partner discovery while keeping the collection workflow simple enough for operations teams.
Agencies and analysts
Client research
Nuanced outcome
Pair this template with the TripAdvisor restaurant scraper, Uber Eats restaurant listings scraper, and Google SERP scraper when a brief needs multiple discovery sources.
How to use
From Tabelog URLs to structured export
1
Download and import
Use the page CTA or the hosted Tabelog store list scraper JSON, then import it into UScraper.
2
Edit the restaurant URLs
Open the Navigate block and replace the sample Tabelog detail URLs with the restaurant pages you are allowed to collect. Add more URLs to expand the batch.
3
Let the page load
The workflow navigates to one URL, waits for page load, then waits until a visible heading element appears before extracting data.
4
Export structured rows
Structured Export reads from the rendered page body, fills the Heading, Restaurant_name, and Restaurant_URL columns, and appends the result to one CSV.
5
Pause and continue the loop
A short sleep gives Tabelog breathing room before Loop Continue advances to the next URL in the list.
Output preview
What the Tabelog CSV export includes
tabelog-store-list-scraper.csvColumn
Heading
Area-style heading inferred from the address, such as a Tokyo ward and neighborhood phrase.
Column
Restaurant_name
The visible restaurant name from the Tabelog detail page heading.
Column
Restaurant_URL
Canonical page URL from Tabelog metadata, with the current page URL as fallback.
Sample rows
2 of many
| Heading | Restaurant_name | Restaurant_URL |
|---|---|---|
| Shinjuku-ku Kagurazakaのお店 | Soba Kappo Example | |
| Toshima-ku Ikebukuroのお店 | Tokyo Izakaya Sample |
Headers included - rows append across configured restaurant URLs - sample values are illustrative
Frequently asked questions
Tabelog pages may be publicly viewable and still governed by Tabelog terms, robots rules, copyright, database rights, privacy law, and local regulations. Use conservative volume, do not bypass access controls, and get legal review before commercial redistribution, resale, or model training.
Limits and maintenance
Before you run a large Tabelog export
Guardrails for reliable Tabelog scraping
Scope
This is a detail-page workflow, not listing pagination
The supplied analysis did not detect a listing-page pagination path. Prepare the restaurant URLs first, then let the multi-URL loop visit each detail page.
Layout
Selectors may need updates after redesigns
Empty names or headings usually mean Tabelog changed heading, address, or metadata markup. Re-test one URL before trusting a long Tabelog restaurant scraper run.
Policy
Review Tabelog rules before commercial use
Check Tabelog terms of use and robots.txt, keep request volume modest, and avoid uses that require permission or licensed data.
Get Started
Download and use this template instantly
$50Free
What's Included
- Template JSON file ready to import
- Pre-configured scraping nodes
- Works with UScraper desktop app
Open-source templates
UScraper templates are open source. Improve this workflow or contribute a new one to help the community grow.
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