Japan Real Estate Intel MCP Server

io.github.sugukurukabe/japan-real-estate-intel-mcp

answering

Japan Real Estate Intel is answering right now. Last checked 7 min ago. 71 installs a week from npm. It exposes 33 tools. Last commit 22 Jul 2026.

Japan real estate MCP: land price, risk, foot traffic, renovation. 10 prefectures.

Installs per day peak 31 · avg 14 · -37% w/w

a month agotoday

Uptime history 40 hours of history · worst hour 67%

40 hours agonow

96.7%

Uptime 24h

88 of 91 checks

Tools

read from the server

660 ms

Response time

average over 24h

Installs / week

npm and PyPI

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 7 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add japan-real-estate-intel-mcp --transport http https://realestate-mcp.jp/mcp

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "japan-real-estate-intel-mcp": {
      "url": "https://realestate-mcp.jp/mcp"
    }
  }
}

~/.codex/config.toml

[mcp_servers.japan-real-estate-intel-mcp]
url = "https://realestate-mcp.jp/mcp"

.cursor/mcp.json

{
  "mcpServers": {
    "japan-real-estate-intel-mcp": {
      "url": "https://realestate-mcp.jp/mcp"
    }
  }
}

.vscode/mcp.json

{
  "mcpServers": {
    "japan-real-estate-intel-mcp": {
      "url": "https://realestate-mcp.jp/mcp"
    }
  }
}

Available tools 33

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

assess

assess_contract_risk

Contract risk assessment: analyze proposed clauses (financing contingency, inspection, future value terms) and return risk score with deal-breakers. | 契約リスク評価。提案中の契約条項を分析しリスクスコアとディールブレーカーを返す。

assess_family_friendly_score

Assess family-friendliness: education, safety, healthcare across 3 axes. 10 prefectures. | ファミリー向け適性評価。教育・安全・医療の3軸で住宅適地を総合評価。全10都道府県。

assess_property_risk

Assess property disaster risk: flood, landslide, earthquake. Integrated scoring across 10 prefectures. | 災害リスク評価。浸水・土砂・地震リスクを統合スコアリング。全10都道府県対応。

simulate

simulate_aichi_future

Aichi future value simulator: Linear Chuo Shinkansen, Centrair 2nd runway, Toyota EV investment, Expo legacy impact on land prices. Markdown report. | 愛知県将来価値シミュレーター。リニア・セントレア・トヨタ・万博レガシーの地価影響をMarkdownレポートで出力。

simulate_landscape_impact

Sunlight/shadow simulation using PLATEAU 3D buildings + SunCalc. | 日照・影シミュレーション。PLATEAU 3D建物データ+SunCalcで周辺建物の影響を分析。

simulate_leveraged_cashflow

Leveraged 10-year real estate pro-forma: accepts loan interest rate, LTV/loan amount, rent, vacancy, operating costs, property tax, depreciation and exit assumptions, then returns annual NOI, debt service, after-tax cash flow, DSCR, IRR, equity multiple and sensitivity. | 銀行借入の利率・LTV・賃料・空室率・経費・固定資産税・減価償却・出口条件から10年の年次収支、税引後CF、DSCR、IRR、感応度を試算する。

generate

generate_area_report

Generate comprehensive area report in Markdown/PDF with branding support. 10 prefectures. | エリアレポート生成。包括的な不動産分析をMarkdown/PDFで出力。ブランディング対応。全10都道府県。

generate_contract_support_package

Contract support package: generate risk matrix, price negotiation anchors, recommended clauses from neighborhood/property data. Markdown + branded PDF. | 売買契約支援パッケージ。リスクマトリックス・価格交渉アンカー・推奨特約を生成。

analyze

analyze_renovation_yield

Renovation yield analysis: calculate acquisition cost, renovation cost, expected rent, gross/net yield for Nagoya neighborhoods. Includes future plan upside. | リノベ利回り分析。名古屋市の町丁目×物件条件から取得価格・リノベ費用・利回りを算出。

area

search_area_candidates

Search municipality name candidates by partial text. Supports hiragana. | 市区町村名の候補検索。部分文字列から有効な市区町村候補を返す。ひらがな対応。

chochou

get_chochou_profile

Neighborhood profile: current metrics (land price, population, households, ongoing plans) for Nagoya wards/neighborhoods. | 町丁目プロファイル。名古屋市の区・町丁目単位の現状指標を返す。

compare

compare_prefectures

Compare up to 5 prefectures: land price, population, risk, investment score ranking. Markdown output. | 都道府県比較。最大5都道府県を横断比較し、地価・人口・リスク・投資スコアをランキング。

composite

composite_value_score

Composite value score: fuse 5 axes (land price, education, transport, future plans, risk) into a single 0-100 score with radar, tier, peer comparison, and AI narrative. | 総合価値スコア。地価・教育・交通・将来計画・リスクを 1 つの 0-100 スコアに融合。レーダー・Tier・ピア比較・AIナラティブ付き。

cross

cross_analyze_real_estate_market

Cross-analyze real estate market: land price trends, investment score, foot traffic, education, corporate presence. 10 prefectures. | 不動産市場クロス分析。地価・投資スコア・人流・教育・企業立地を総合分析。10都道府県対応。

detect

detect_arbitrage_signals

Price triangulation arbitrage scanner: cross-checks 路線価(rosenka) × 公示地価(koji) × 取引価格(tx) to detect discount buys, inheritance-tax edges, and overheated markets. | 路線価・公示地価・取引価格の三角測量でディスカウント物件・相続有利エリア・市場過熱を検出する。

discover

discover_opportunities

Opportunity Radar: scan a prefecture for undervalued areas matching your goal (investment/store/family/office/development). Returns hypothesis cards with multi-source scoring. | Opportunity Radar。都道府県内を横断スキャンし、目的に応じた次に見るべきエリア仮説カードを返す。

drill

drill_down_local_analysis

Drill-down local analysis at block/neighborhood level including foot traffic, commercial, education. Markdown output. | 街区ドリルダウン分析。町丁目レベルの詳細分析。Markdown出力。

evaluate

evaluate_store_location

Evaluate store location suitability considering foot traffic, transport, competitor distribution. 10 prefectures. | 店舗出店適地評価。人流・交通・競合店分布を考慮したスコアを算出。全10都道府県。

fetch

fetch

Fetch full document by ID from search results. Returns area analysis, forecasts, and summaries in Markdown. | 検索結果のIDからドキュメント全文を取得する。分析レポート・将来予測・データサマリをMarkdownで返す。

forecast

forecast_land_price_trend

Forecast land price trends using linear regression and moving average. Returns CAGR, confidence interval, investment signal (buy/hold/caution). 10 prefectures. | 地価トレンド予測。線形回帰・移動平均で将来地価を予測。CAGR・投資シグナルを返す。全10都道府県。

future

get_future_timeline

Future timeline: upcoming redevelopment, infrastructure, and population projections for Nagoya wards/neighborhoods (2025-2050). | 未来タイムライン。名古屋市の区・町丁目に影響する将来計画を年次タイムラインで返す。

open

open_dashboard

Open visualization dashboard. 2D map or PLATEAU 3D view. MCP Apps UI. | 可視化ダッシュボードを開く。2Dマップ/PLATEAU 3Dビュー。MCP Apps UI対応。

population

get_population_outlook

Population outlook to 2050 (将来人口推計): projected population at 2030/2040/2050 with decline rate, based on NIPSSR data. | 2030/2040/2050年の人口推計と減少率を返す。

portfolio

portfolio_optimizer

Optimize real estate investment portfolio across up to 5 areas. Returns expected return, risk score, Sharpe ratio. | 不動産投資ポートフォリオ最適化。最大5エリアのリターン・リスク・シャープレシオを算出。

predict

predict_corporate_demand

Predict corporate demand: manufacturing, office, retail demand scores. 10 prefectures. | 企業立地需要予測。製造業・オフィス・小売の企業需要スコアを算出。全10都道府県。

quick

quick_visual_summary

Render a ChatGPT-optimized real estate visual summary with map, charts, recommended next actions, and compact markdown fallback. Always use this when the user asks to show, visualize, compare, or continue in ChatGPT. | ChatGPT向けに地図・グラフ・次アクション・要約をまとめて表示するレンダーツール。

real

get_real_estate_macro_snapshot

One-screen macro view: land price YoY (median ㎡/year), transaction counts (last 3y), population decline to 2050; optional e-Stat building construction starts by prefecture (needs ESTAT_APP_ID) and FRED policy-rate proxy CSV. | 地価中央値YoY・取引件数・2050人口減、e-Stat建築着工・FRED短期金利プロキシを一枚に。

recommend

recommend_renovation_targets

Renovation yield ranking: scan all 16 Nagoya wards to rank neighborhoods by yield. | リノベ利回りランキング。名古屋市全16区の主要町丁目を横断スキャンし利回り上位をランキング。

review

review_purchase_recommendation

Real estate purchase review for executives: evaluates asking price vs 公示地価/路線価/取引相場, yield (gross/net), risk (vacancy/aging/disaster), future potential, and contract terms. Returns 5-axis scores, decision (buy/negotiate/hold/reject), red flags, negotiation points, and recommended clauses. | 不動産屋経営者向け購入審査：販売価格 vs 公示地価・路線価・取引相場、利回り、リスク、将来性、契約条件を5軸評価。判断（購入/交渉/保留/非推奨）、レッドフラグ、交渉ポイント、推奨特約条項を返却。

scenario

scenario_what_if

What-If scenario analysis: simulate impact of new stations, commercial facilities, population changes on land prices and investment scores. 10 prefectures. | シナリオWhat-If分析。新駅・大型商業施設・人口変動の地価影響を試算。全10都道府県。

search

Search the real estate data catalog for areas, tools, and data sources. ChatGPT-compatible. | 不動産データカタログを検索し、関連するエリア・ツール・データソースの候補一覧を返す。

vacancy

get_vacancy_stats

Vacancy rate statistics (空き家率) by municipality: total vacant, for-rent, for-sale, other — compared to national average. | 市区町村別の空き家率・種類別内訳を全国平均と比較して返す。

zoning

get_zoning_info

Look up zoning (用途地域) for an area: zone type, coverage ratio (建蔽率), floor area ratio (容積率), and height limits. | 用途地域・建蔽率・容積率・高さ制限を返す。

Endpoints

URL	Transport	State	Latency	Checked
https://realestate-mcp.jp/mcp	streamable-http	answering	712 ms	7 min ago

Japan Real Estate Intel — questions

Answers built from our own checks of this server.

What can Japan Real Estate Intel do?

It exposes 33 tools, read directly from the server on our last check. Among them: analyze_renovation_yield, assess_contract_risk, assess_family_friendly_score, assess_property_risk, compare_prefectures, composite_value_score and 27 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →

What is Japan Real Estate Intel mostly used for?

Its tools cluster around simulate, assess and generate. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.

Is Japan Real Estate Intel working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 88 of 91 checks got a reply (96.7%), average response time 660 ms. The bar chart above shows every period we have measured.

How do I connect Japan Real Estate Intel?

Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.

Does Japan Real Estate Intel need an API key?

No. Japan Real Estate Intel completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 33 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is Japan Real Estate Intel?

It answers our handshake in 660 ms on average, which is faster than 13% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.

How many people use Japan Real Estate Intel?

The npm package @sugukuru/japan-real-estate-intel-mcp was installed 71 times in the last week. Week over week that is -37%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.

Is Japan Real Estate Intel open source?

Yes — it is published under the AGPL-3.0 licence, written in HTML and 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.