mcp server
Japan Real Estate Intel
Japan real estate MCP: land price, risk, foot traffic, renovation. 10 prefectures.
Description as published by the maintainer. Source
- version 6.15.1
- active
active — Registry entry last updated 2026-05-19.
What this server can do
33 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
analyze_renovation_yield(ward, chochou, floorArea, buildingAge, propertyType, acquisitionPrice)- 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。 Required: ward, chochou, buildingAge, floorArea.
assess_contract_risk(ward, chochou, proposedTerms)- 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。 Required: ward, proposedTerms.
assess_family_friendly_score(area, latlng, address, childAge, prefecture, neighborhood)- 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。 Required: area.
assess_property_risk(latlng, address, riskTypes, prefecture, neighborhood)- 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。 Required: address.
compare_prefectures(area, metrics, prefectures, exportFormat, neighborhood, propertyType, includeMarkdown)- 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。 Required: prefectures.
composite_value_score(area, horizon, weights, prefecture, output_mode, includeMarkdown, includeNarrative)- 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。 Required: area.
cross_analyze_real_estate_market(area, timeRange, prefecture, includeRisk, output_mode, focusMetrics, neighborhood, propertyType, includeMedical, includeCorporate, includeEducation, includeHumanFlow, includeTransport, includeCommercial)- 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。 Required: area, propertyType, timeRange.
detect_arbitrage_signals(limit, prefecture, signalType, includeLive, output_mode)- 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_opportunities(goal, limit, horizon, prefecture, budgetLevel, output_mode, riskTolerance, includeMarkdown, useGeminiNarrative, includeExternalFreshness)- 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_down_local_analysis(city, focus, prefecture, exportFormat, neighborhood)- 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。 Required: city.
evaluate_store_location(city, radiusM, storeType, prefecture, neighborhood, customWeights, includeMarkdown)- 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。 Required: city, storeType.
fetch(id)- 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。 Required: id.
forecast_land_price_trend(city, method, horizon, landUse, prefecture, output_mode, includeMarkdown)- 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。 Required: city.
generate_area_report(area, format, purpose, agentName, disclaimer, prefecture, companyName, neighborhood, footerContact, includeCharts, agentLogoBase64, includeLinearImpact, includeTransactionComparables)- 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。 Required: area, purpose.
generate_contract_support_package(ward, price, chochou, floorArea, buildingAge, propertyType, proposedClauses)- 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。 Required: ward, buildingAge, floorArea, price.
get_chochou_profile(ward, chochou, output_mode)- 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。 Required: ward.
get_future_timeline(ward, chochou)- 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。 Required: ward.
get_population_outlook(area, prefecture)- 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。
get_real_estate_macro_snapshot(area, prefecture, includeExternalSeries)- 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。
get_vacancy_stats(area, prefecture)- 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。
get_zoning_info(area, district, prefecture)- 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。 Required: area.
open_dashboard(area, mode, layer, prefecture, initialMode, neighborhood, propertyType)- 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。
portfolio_optimizer(targets, optimizeFor, riskTolerance, includeMarkdown, investmentHorizon)- 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。 Required: targets.
predict_corporate_demand(area, prefecture, neighborhood, propertyType, includeCommuteAnalysis)- 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。 Required: area.
quick_visual_summary(area, mode, intent, compact, prefecture)- 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。
recommend_renovation_targets(limit, floorArea, buildingAge, propertyType)- 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_purchase_recommendation(city, floors, district, structure, prefecture, addressMemo, askingPrice, buildingAge, landAreaSqm, output_mode, propertyType, occupancyRate, proposedTerms, renovationCost, buildingAreaSqm, kojiPricePerSqm, negotiablePrice, exclusiveAreaSqm, recommenderClaim, currentAnnualRent, propertyTaxAnnual, expectedAnnualRent, rosenkaPricePerSqm, operatingExpenseAnnual, transactionMedianPerSqm)- 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。 Required: city, askingPrice.
scenario_what_if(city, scale, horizon, scenario, prefecture, includeMarkdown)- 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。 Required: city, scenario.
search(query)- 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。 Required: query.
search_area_candidates(limit, query, prefecture)- 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。
simulate_aichi_future(city, horizon, scenarios, includeMarkdown)- 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。 Required: city.
simulate_landscape_impact(lat, lng, radiusM, dateTime, prefecture, timePreset, includeMarkdown)- 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。 Required: lat, lng.
simulate_leveraged_cashflow(city, loan, district, annualRent, prefecture, annualCapex, askingPrice, assumptions, output_mode, vacancyRate, propertyType, purchaseCost, landValueRatio, renovationCost, otherIncomeAnnual, propertyTaxAnnual, operatingExpenseAnnual)- 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。 Required: city, askingPrice, annualRent, loan.
Last successful function declaration observed on . Source: https://realestate-mcp.jp/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://realestate-mcp.jp/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| Latest published version | 6.15.1 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-05-19 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-05-19 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| mcp tools declared | 33 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | realestate-mcp.jp | |
| mcp endpoint status | ok | The server listed 33 functions when asked. | as of probe | realestate-mcp.jp |
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