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Tabelog MCP Server

Tabelog MCP Server

by horse-3903
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Tabelog MCP Server

Unofficial Tabelog MCP server: let AI assistants search Japanese restaurants, read reviews and menus through the Tabelog Inbound app API.

Python MCP License

Overview

tabelog-mcp is a Model Context Protocol server for Tabelog. It wraps the unofficial Tabelog API documented in tabelog-api-spec.

Status: the anonymous token request, area lookup, keyword suggestions, restaurant search, detail, reviews, menu, photos and nearby popular items were checked live and return data. Other tools and the tabelog_get escape hatch are untested. The server uses requests, because the same token request from httpx was answered with HTTP 403 by Tabelog's Cloudflare.

Related MCP server: tabelog

Features

  • Search - restaurants by area, genre or coordinates, plus keyword suggestions

  • Detail - restaurant info, reviews, menus, photos and vacancy

  • Areas - prefecture, area and station lookups

  • Read-only - no login and no writes; tabelog_get calls any GET endpoint from the spec

Tech Stack

Python MCP

Getting Started

Prerequisites

  • Python 3.10+

Installation

git clone https://github.com/horse-3903/tabelog-mcp
cd tabelog-mcp
pip install .

Run locally

tabelog-mcp

Add to Claude Desktop or Claude Code

{
  "mcpServers": {
    "tabelog": { "command": "tabelog-mcp" }
  }
}

FAQ

Is there an official Tabelog MCP server? No. This one is unofficial and uses the reverse engineered Tabelog Inbound app API from tabelog-api-spec.

Does it need a Tabelog account? No. It requests an anonymous device token on first use.

Disclaimer

Unofficial and not affiliated with Tabelog or Kakaku.com. Use at your own risk. Calling the API outside the official app may violate the service's terms and can lead to blocking or a suspended account. The authors accept no responsibility for any consequences.

Available Tools

12 tools
area_level1C

List first level areas for a prefecture.

Inputs: prefecture_id (required).

area_level2C

List second level areas inside a first level area.

Inputs: area1_id (required).

popular_nearbyC

Get popular items near a coordinate.

Inputs: latitude (required), longitude (required).

restaurant_detailC

Get details for one restaurant.

Inputs: restaurant_id (required).

restaurant_menuC

Get a restaurant's menu list.

Inputs: restaurant_id (required).

restaurant_photosC

Get a restaurant's photos.

Inputs: restaurant_id (required), page.

restaurant_reviewsC

Get reviews for a restaurant.

Inputs: restaurant_id (required), page.

restaurant_vacancyC

Get vacancy status for a restaurant over the next few days.

Inputs: restaurant_id (required).

search_restaurantsB

Search Japanese restaurants. search_type is normal, current or map; area_datatype is area1, area2, majorCity, municipal, prefecture or station; genre_datatype is genre0 to genre3; sort_mode is totalRanking, distance, accessRanking, reservationRanking or domesticMostReserved. Get ids from the area tools and suggest_search.

Inputs: page, range, latitude, longitude, +6 more.

stationsC

List stations in a second level area.

Inputs: area2_id (required).

suggest_searchC

Autocomplete restaurants, areas and genres for a keyword.

Inputs: keyword (required), current_latitude, current_longitude.

tabelog_getB

Read-only escape hatch: GET any endpoint name under /tabelog_inbound_appli/ from the Tabelog spec (see tabelog-api-spec).

Inputs: endpoint (required), params.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3/5.0

    Scored across 12 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes: restaurant detail vs reviews/menu/photos/vacancy, hierarchical area tools, and search/autocomplete. The only overlap is tabelog_get, which can reach any GET endpoint and could compete with dedicated tools, but it is explicitly framed as an escape hatch.

    Naming Consistency3/5

    The names use several conventions: verb_noun (search_restaurants, suggest_search), resource_noun (restaurant_reviews), noun_detail (restaurant_detail), area_level1/2, and bare nouns like stations. They remain readable, but the pattern is not predictable.

    Tool Count5/5

    12 tools is well-scoped for a restaurant discovery/detail server. Each tool maps to a meaningful step: location resolution, search/autocomplete, restaurant details, subresources, nearby popularity, and a generic read escape hatch.

    Completeness4/5

    The surface covers area hierarchy, stations, autocomplete, search, restaurant detail, reviews, menu, photos, vacancy, and nearby popularity. Minor gaps include an explicit genre taxonomy listing and reservation/booking actions, but the read-only escape hatch mitigates missing GET coverage.

    Maintenance

    ActivityMaintained
    ResponsivenessOutdated data

    Resources

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