# Japan Real Estate Intel (remote · realestate-mcp.jp)

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

- Trust score: 46/100 (low)
- Change this week: +15
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `realestate-mcp.jp`: 46/100 (this document), [markdown](https://verifymcp.io/servers/sugukurukabe-japan-real-estate-intel-mcp/realestate-mcp.md), [page](https://verifymcp.io/servers/sugukurukabe-japan-real-estate-intel-mcp/realestate-mcp)
- npm · `@sugukuru/japan-real-estate-intel-mcp`: 35/100, [markdown](https://verifymcp.io/servers/sugukurukabe-japan-real-estate-intel-mcp/sugukuru-japan-real-estate-intel-mcp.md), [page](https://verifymcp.io/servers/sugukurukabe-japan-real-estate-intel-mcp/sugukuru-japan-real-estate-intel-mcp)

## Channel facts

- Endpoint: `https://realestate-mcp.jp/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `6.15.1`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 89/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - The endpoint enforces authorisation, but returns a challenge with no valid RFC 9728 metadata, so a client cannot discover where to get a token.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 0/100
  - Schema blocked by authentication: the endpoint requires auth we don't have to read it.
- **Stability & Change Management**: 0/100
  - Stability not yet verified: not enough scan history yet (needs a 30-day window).
- **Tool Coverage**: 0/100
  - Tool coverage blocked by authentication: the endpoint requires auth we don't have to read its tools.
- **Capabilities**: 0/100
  - Capabilities blocked by authentication: the endpoint requires auth we don't have to read them.

**Unverified: 4 categories.** Categories scored 0 because we could not verify them: authentication we do not have, an unreachable endpoint, or not enough scan history. We only credit what we can confirm.

## Install

### Claude

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

### Codex

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

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "sugukurukabe-japan-real-estate-intel-mcp": {
      "type": "remote",
      "url": "https://realestate-mcp.jp/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add sugukurukabe-japan-real-estate-intel-mcp --url https://realestate-mcp.jp/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  sugukurukabe-japan-real-estate-intel-mcp:
    url: "https://realestate-mcp.jp/mcp"
```

### Other

```json
{
  "mcpServers": {
    "sugukurukabe-japan-real-estate-intel-mcp": {
      "type": "http",
      "url": "https://realestate-mcp.jp/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 46, −24)

- [security regression] Endpoint reachability: reachable → behind authorisation
- [security regression] Stability: fail → unverified
- [security regression] Authorization: partial → fail
- [functional regression] Capabilities: pass → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional regression] Schema quality: 100 → unverified

### 2026-08-02 (score 70, −7)

- [security regression] Authorization: fail → unverified
- [security regression] Stability: 0.20 → fail
- [security regression] A breaking change shipped without a version bump: still 6.16.0
- [security regression] Tool “get_zoning_info” was removed
- [security regression] Tool “open_dashboard” was removed
- [security regression] Tool “portfolio_optimizer” was removed
- [security regression] Tool “predict_corporate_demand” was removed
- [security regression] Tool “quick_visual_summary” was removed
- [security regression] Tool “recommend_renovation_targets” was removed
- [security regression] Tool “review_purchase_recommendation” was removed
- [security regression] Tool “scenario_what_if” was removed
- [security regression] Tool “search” was removed
- [security regression] Tool “search_area_candidates” was removed
- [security regression] Tool “simulate_landscape_impact” was removed
- [security regression] Tool “simulate_leveraged_cashflow” was removed
- [security regression] Tool “simulate_aichi_future” was removed
- [security regression] Tool “assess_contract_risk” was removed
- [security regression] Tool “assess_family_friendly_score” was removed
- [security regression] Tool “assess_property_risk” was removed
- [security regression] Tool “analyze_renovation_yield” was removed
- [security regression] Tool “compare_prefectures” was removed
- [security regression] Tool “composite_value_score” was removed
- [security regression] Tool “cross_analyze_real_estate_market” was removed
- [security regression] Tool “detect_arbitrage_signals” was removed
- [security regression] Tool “discover_opportunities” was removed
- [security regression] Tool “drill_down_local_analysis” was removed
- [security regression] Tool “evaluate_store_location” was removed
- [security regression] Tool “fetch” was removed
- [security regression] Tool “forecast_land_price_trend” was removed
- [security regression] Tool “generate_area_report” was removed
- [security regression] Tool “generate_contract_support_package” was removed
- [security regression] Tool “get_chochou_profile” was removed
- [security regression] Tool “get_future_timeline” was removed
- [security regression] Tool “get_population_outlook” was removed
- [security regression] Tool “get_real_estate_macro_snapshot” was removed
- [security regression] Tool “get_vacancy_stats” was removed
- [security improvement] Authorization: fail → partial
- [functional regression] Schema quality: fail → unverified
- [functional regression] Schema quality: good → unverified
- [functional regression] Schema quality: fail → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional regression] Prompt “aichi_future_value” was removed
- [functional regression] Prompt “arbitrage_scan” was removed
- [functional regression] Prompt “composite_value_report” was removed
- [functional regression] Prompt “investment_report” was removed
- [functional regression] Prompt “land_price_forecast_report” was removed
- [functional regression] Prompt “opportunity_radar” was removed
- [functional regression] Prompt “population_outlook_report” was removed
- [functional regression] Prompt “portfolio_optimization” was removed
- [functional regression] Prompt “prefecture_comparison” was removed
- [functional regression] Prompt “quick_start_examples” was removed
- [functional regression] Prompt “scenario_what_if_analysis” was removed
- [functional regression] Prompt “store_location_evaluation” was removed
- [functional regression] Prompt “vacancy_analysis” was removed
- [functional regression] Prompt “zoning_check” was removed
- [functional] New tool “drill_down_local_analysis”
- [functional] New tool “discover_opportunities”
- [functional] New tool “detect_arbitrage_signals”
- [functional] New tool “cross_analyze_real_estate_market”
- [functional] New tool “composite_value_score”
- [functional] New tool “compare_prefectures”
- [functional] New tool “assess_property_risk”
- [functional] New tool “assess_family_friendly_score”
- [functional] New tool “assess_contract_risk”
- [functional] New tool “analyze_renovation_yield”
- [functional] New tool “simulate_leveraged_cashflow”
- [functional] New tool “simulate_landscape_impact”
- [functional] New tool “simulate_aichi_future”
- [functional] New tool “search_area_candidates”
- [functional] New tool “search”
- [functional] New tool “get_future_timeline”
- [functional] New tool “get_chochou_profile”
- [functional] New tool “generate_contract_support_package”
- [functional] New tool “generate_area_report”
- [functional] New tool “forecast_land_price_trend”
- [functional] New tool “fetch”
- [functional] New tool “evaluate_store_location”
- [functional] New tool “scenario_what_if”
- [functional] New tool “review_purchase_recommendation”
- [functional] New tool “recommend_renovation_targets”
- [functional] New tool “quick_visual_summary”
- [functional] New tool “predict_corporate_demand”
- [functional] New tool “portfolio_optimizer”
- [functional] New tool “open_dashboard”
- [functional] New tool “get_zoning_info”
- [functional] New tool “get_vacancy_stats”
- [functional] New tool “get_real_estate_macro_snapshot”
- [functional] New tool “get_population_outlook”

### 2026-08-01 (score 77, +26)

- [functional regression] Schema quality: unverified → fail
- [functional regression] Schema quality: unverified → fail
- [functional improvement] Schema quality: unverified → good
- [functional improvement] Tool coverage: unverified → 100
- [functional] Stability: fail → 0.20
- [functional] New prompt “land_price_forecast_report”
- [functional] New prompt “population_outlook_report”
- [functional] New prompt “zoning_check”
- [functional] New prompt “vacancy_analysis”
- [functional] New prompt “store_location_evaluation”
- [functional] New prompt “scenario_what_if_analysis”
- [functional] New prompt “quick_start_examples”
- [functional] New prompt “opportunity_radar”
- [functional] New prompt “prefecture_comparison”
- [functional] New prompt “portfolio_optimization”
- [functional] New prompt “investment_report”
- [functional] New prompt “composite_value_report”
- [functional] New prompt “arbitrage_scan”
- [functional] New prompt “aichi_future_value”
- [functional] New tool “generate_contract_support_package”
- [functional] New tool “analyze_renovation_yield”
- [functional] New tool “assess_contract_risk”
- [functional] New tool “assess_family_friendly_score”
- [functional] New tool “assess_property_risk”
- [functional] New tool “compare_prefectures”
- [functional] New tool “composite_value_score”
- [functional] New tool “cross_analyze_real_estate_market”
- [functional] New tool “detect_arbitrage_signals”
- [functional] New tool “discover_opportunities”
- [functional] New tool “drill_down_local_analysis”
- [functional] New tool “evaluate_store_location”
- [functional] New tool “fetch”
- [functional] New tool “forecast_land_price_trend”
- [functional] New tool “generate_area_report”
- [functional] New tool “get_chochou_profile”
- [functional] New tool “simulate_leveraged_cashflow”
- [functional] New tool “simulate_landscape_impact”
- [functional] New tool “simulate_aichi_future”
- [functional] New tool “search_area_candidates”
- [functional] New tool “search”
- [functional] New tool “scenario_what_if”
- [functional] New tool “review_purchase_recommendation”
- [functional] New tool “recommend_renovation_targets”
- [functional] New tool “quick_visual_summary”
- [functional] New tool “predict_corporate_demand”
- [functional] New tool “portfolio_optimizer”
- [functional] New tool “open_dashboard”
- [functional] New tool “get_zoning_info”
- [functional] New tool “get_vacancy_stats”
- [functional] New tool “get_real_estate_macro_snapshot”
- [functional] New tool “get_population_outlook”
- [functional] New tool “get_future_timeline”

### 2026-07-31 (score 51, +20)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 31, 0)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-29 (score 31, −9)

- [functional regression] Prompt “prefecture_comparison” was removed
- [functional regression] Prompt “quick_start_examples” was removed
- [functional regression] Prompt “scenario_what_if_analysis” was removed
- [functional regression] Prompt “store_location_evaluation” was removed
- [functional regression] Prompt “vacancy_analysis” was removed
- [functional regression] Prompt “zoning_check” was removed
- [functional regression] Prompt “land_price_forecast_report” was removed
- [functional regression] Prompt “aichi_future_value” was removed
- [functional regression] Prompt “composite_value_report” was removed
- [functional regression] Prompt “arbitrage_scan” was removed
- [functional regression] Prompt “investment_report” was removed
- [functional regression] Prompt “opportunity_radar” was removed
- [functional regression] Prompt “population_outlook_report” was removed
- [functional regression] Prompt “portfolio_optimization” was removed

### 2026-07-28 (score 40, +9)

- [functional] First check of Schema quality: 100
- [functional] New prompt “quick_start_examples”
- [functional] New prompt “prefecture_comparison”
- [functional] New prompt “scenario_what_if_analysis”
- [functional] New prompt “zoning_check”
- [functional] New prompt “vacancy_analysis”
- [functional] New prompt “store_location_evaluation”
- [functional] New prompt “aichi_future_value”
- [functional] New prompt “arbitrage_scan”
- [functional] New prompt “composite_value_report”
- [functional] New prompt “investment_report”
- [functional] New prompt “land_price_forecast_report”
- [functional] New prompt “opportunity_radar”
- [functional] New prompt “population_outlook_report”
- [functional] New prompt “portfolio_optimization”

### 2026-07-27 (score 31, −33)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

## MCP tools (33)

### `search` (~99 tokens)

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。

Input parameters:

- `query` (string, required): 検索クエリ（自然文OK。例: "名古屋 リニア", "東京 投資", "リスク 地震"）

### `fetch` (~100 tokens)

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。

Input parameters:

- `id` (string, required): search ツールで取得したドキュメントID（例: "area:aichi:名古屋市中区"）

### `search_area_candidates` (~128 tokens)

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。

Input parameters:

- `limit` (integer): 最大候補数（1-20、デフォルト20）
- `prefecture` (string): 都道府県名（例: 愛知県, 東京都）
- `query` (string): 市区町村名の一部（例: 名古屋, なごやしなか, 新宿）

### `cross_analyze_real_estate_market` (~425 tokens)

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。

Input parameters:

- `area` (string, required): エリア（例: '名古屋市中村区', '世田谷区'）
- `focusMetrics` (array)
- `includeCommercial` (boolean): 商業施設データを含むか（対応都道府県のみ）
- `includeCorporate` (boolean): 企業立地データを含むか（対応都道府県のみ）
- `includeEducation` (boolean): 教育環境データを含むか（対応都道府県のみ）
- `includeHumanFlow` (boolean): 人流データを含むか（対応都道府県のみ）
- `includeMedical` (boolean): 医療・福祉施設データを含むか（対応都道府県のみ）
- `includeRisk` (boolean): 災害リスクを考慮するか
- `includeTransport` (boolean): 交通利便性データを含むか（対応都道府県のみ）
- `neighborhood` (string): 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ）
- `output_mode` (string): Output verbosity. compact=TL;DR + key numbers only (default), detailed=full Markdown report | 出力詳細度。compact=主要数値のみ（デフォルト）、detailed=全文レポート付き
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `propertyType` (string, required)
- `timeRange` (string, required)

### `assess_property_risk` (~166 tokens)

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。

Input parameters:

- `address` (string, required): 住所または地番
- `latlng` (object)
- `neighborhood` (string): 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ）
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `riskTypes` (array)

### `assess_family_friendly_score` (~180 tokens)

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。

Input parameters:

- `address` (string): 具体的な住所（任意）
- `area` (string, required): エリア
- `childAge` (string)
- `latlng` (object)
- `neighborhood` (string): 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ）
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）

### `predict_corporate_demand` (~172 tokens)

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。

Input parameters:

- `area` (string, required): エリア
- `includeCommuteAnalysis` (boolean): 通勤時間分析を含むか
- `neighborhood` (string): 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ）
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `propertyType` (string)

### `generate_area_report` (~342 tokens)

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。

Input parameters:

- `agentLogoBase64` (string): 会社ロゴ画像（Base64 Data URL）
- `agentName` (string): 担当者名（PDFヘッダーに表示）
- `area` (string, required)
- `companyName` (string): 会社名（PDFヘッダーに表示）
- `disclaimer` (string): 免責文言（PDF末尾に追加）
- `footerContact` (string): 連絡先（PDF末尾フッター）
- `format` (string): 出力フォーマット。pdf を指定すると pdfBase64 フィールドに Base64 エンコード済み PDF を返す
- `includeCharts` (boolean)
- `includeLinearImpact` (boolean): リニア中央新幹線の影響試算を含めるか（愛知県のみ）
- `includeTransactionComparables` (boolean): 過去取引事例テーブルを含めるか
- `neighborhood` (string): 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ）
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `purpose` (string, required)

### `open_dashboard` (~234 tokens)

不動産ダッシュボード

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。

Input parameters:

- `area` (string): 初期表示エリア
- `initialMode` (string): デュアルモード切替。investment=不動産投資モード（デフォルト）、store=店舗出店戦略モード
- `layer` (string): 初期レイヤー
- `mode` (string): ダッシュボード表示モード。3dを指定するとPLATEAU 3Dビューアを開く
- `neighborhood` (string): 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ）
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `propertyType` (string)

Output parameters:

- `area` (string)
- `attribution` (string)
- `dashboardUrl` (string)
- `initialMode` (string)
- `layer` (string)
- `mode` (string)
- `prefecture` (string)

### `quick_visual_summary` (~188 tokens)

ChatGPTビジュアル要約

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。

Input parameters:

- `area` (string): Target area to focus the visual summary on | 表示対象エリア
- `compact` (boolean): Optimize copy and layout for ChatGPT mobile/compact views
- `intent` (string): User goal for choosing the best visual starting point | 表示目的
- `mode` (string): Dashboard mode | ダッシュボード表示モード
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）

Output parameters:

- `area` (string)
- `attribution` (string)
- `dashboardUri` (string): MCP Apps ui:// resource URI
- `dashboardUrl` (string): Browser fallback URL or path
- `intent` (string)
- `layer` (string)
- `markdownReport` (string): Compact markdown fallback for non-UI clients
- `mode` (string)
- `nextActions` (array)
- `prefecture` (string)
- `summary` (string)
- `title` (string)

### `compare_prefectures` (~245 tokens)

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。

Input parameters:

- `area` (string): 各都道府県の代表エリア（省略時は県庁所在地相当。愛知=名古屋市中区、東京=千代田区）
- `exportFormat` (string): 出力フォーマット。xlsx を指定すると xlsxBase64 フィールドに Base64 エンコード済み Excel を返す
- `includeMarkdown` (boolean)
- `metrics` (array)
- `neighborhood` (string): 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ）
- `prefectures` (array, required): 比較対象都道府県（2-5県）。例: ["愛知県", "東京都"]
- `propertyType` (string)

### `drill_down_local_analysis` (~192 tokens)

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。

Input parameters:

- `city` (string, required): 市区町村（例: '名古屋市中村区'）
- `exportFormat` (string): 出力フォーマット。xlsx を指定すると xlsxBase64 フィールドに Base64 エンコード済み Excel を返す
- `focus` (string)
- `neighborhood` (string): 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ）
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）

### `evaluate_store_location` (~231 tokens)

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。

Input parameters:

- `city` (string, required): 市区町村（例: '名古屋市中村区'）
- `customWeights` (object): カスタム重み付け（省略時はタイプ別デフォルト）
- `includeMarkdown` (boolean)
- `neighborhood` (string): 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ）
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `radiusM` (number): 競合・施設を検索する半径（メートル）
- `storeType` (string, required): 出店を検討する店舗タイプ

### `simulate_landscape_impact` (~204 tokens)

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。

Input parameters:

- `dateTime` (string): シミュレーション日時（ISO 8601形式、省略時は現在時刻）
- `includeMarkdown` (boolean)
- `lat` (number, required): 対象地点の緯度
- `lng` (number, required): 対象地点の経度
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `radiusM` (number): 建物検索半径（メートル）
- `timePreset` (string): 時刻プリセット（morning=8:00, noon=12:00, evening=17:00）

### `forecast_land_price_trend` (~266 tokens)

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。

Input parameters:

- `city` (string, required): 市区町村（例: '名古屋市中村区', '世田谷区'）
- `horizon` (string): 予測期間
- `includeMarkdown` (boolean)
- `landUse` (string): 地目フィルター。all=全地目平均
- `method` (string): 予測手法。linear=線形回帰、moving_avg=移動平均外挿
- `output_mode` (string): Output verbosity. compact=TL;DR + key numbers only (default), detailed=full Markdown report | 出力詳細度。compact=主要数値のみ（デフォルト）、detailed=全文レポート付き
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）

### `scenario_what_if` (~177 tokens)

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。

Input parameters:

- `city` (string, required): 市区町村（例: '名古屋市中村区'）
- `horizon` (string)
- `includeMarkdown` (boolean)
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `scale` (string): 規模感。large=大型施設・急成長など
- `scenario` (string, required): シナリオ種別

### `portfolio_optimizer` (~130 tokens)

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。

Input parameters:

- `includeMarkdown` (boolean)
- `investmentHorizon` (string): 投資期間
- `optimizeFor` (string): 最適化目標
- `riskTolerance` (string): リスク許容度
- `targets` (array, required): 比較対象エリア（2〜5件）

### `simulate_aichi_future` (~168 tokens)

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。

Input parameters:

- `city` (string, required): 対象市区町村（例: 名古屋市中区, 豊田市, 常滑市）
- `horizon` (string): 試算期間
- `includeMarkdown` (boolean)
- `scenarios` (array): シナリオ（all で全シナリオを一括試算）

### `discover_opportunities` (~327 tokens)

Opportunity Radar

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。

Input parameters:

- `budgetLevel` (string): 想定予算帯。low=㎡15万以下, middle=15-50万, high=50万超
- `goal` (string): 探索目的
- `horizon` (string)
- `includeExternalFreshness` (boolean): true かつ MLIT_API_KEY 環境変数があるとき、MLIT API から最新取引を取得しシグナルに反映
- `includeMarkdown` (boolean)
- `limit` (integer): 返却する候補数
- `output_mode` (string): Output verbosity. compact=TL;DR + key numbers only (default), detailed=full Markdown report | 出力詳細度。compact=主要数値のみ（デフォルト）、detailed=全文レポート付き
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `riskTolerance` (string)
- `useGeminiNarrative` (boolean): true かつ GOOGLE_GENAI_API_KEY があるとき、Gemini でカードに creativeAngle と質問候補を追加

Output parameters:

- `attribution` (string)
- `cards` (array)
- `dataCoverage` (object)
- `markdownReport` (string)
- `nextActions` (array)
- `summary` (string)

### `analyze_renovation_yield` (~187 tokens)

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。

Input parameters:

- `acquisitionPrice` (number): 取得予定価格 (円)。省略時は推定
- `buildingAge` (number, required): 築年数
- `chochou` (string, required): 町丁目名 (例: 栄三丁目, 名駅一丁目)
- `floorArea` (number, required): 専有面積 (㎡)
- `propertyType` (string): 物件種別
- `ward` (string, required): 名古屋市の区名 (例: 中区, 中村区)

### `get_future_timeline` (~109 tokens)

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。

Input parameters:

- `chochou` (string): 町丁目名 (省略時は区全体)
- `ward` (string, required): 名古屋市の区名 (例: 中区)

### `get_chochou_profile` (~152 tokens)

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。

Input parameters:

- `chochou` (string): 町丁目名 (省略時は区全体)
- `output_mode` (string): Output verbosity. compact=TL;DR + key numbers only (default), detailed=full Markdown report | 出力詳細度。compact=主要数値のみ（デフォルト）、detailed=全文レポート付き
- `ward` (string, required): 名古屋市の区名

### `recommend_renovation_targets` (~116 tokens)

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。

Input parameters:

- `buildingAge` (number): 想定築年数
- `floorArea` (number): 想定面積 (㎡)
- `limit` (number): 上位何件を返すか
- `propertyType` (string)

### `generate_contract_support_package` (~191 tokens)

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。

Input parameters:

- `buildingAge` (number, required): 築年数
- `chochou` (string): 町丁目名 (省略時は区全体)
- `floorArea` (number, required): 専有面積 (㎡)
- `price` (number, required): 取得予定価格 (円)
- `propertyType` (string): 物件種別
- `proposedClauses` (array): すでに検討中の特約・条項（任意）
- `ward` (string, required): 名古屋市の区名 (例: 中区, 中村区)

### `assess_contract_risk` (~118 tokens)

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。

Input parameters:

- `chochou` (string): 町丁目名
- `proposedTerms` (object, required): 提案中の契約条項（JSON 形式）
- `ward` (string, required): 名古屋市の区名

### `composite_value_score` (~319 tokens)

総合価値スコア

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。

Input parameters:

- `area` (string, required): Target area (e.g. '名古屋市中区', '新宿区') | 対象エリア
- `horizon` (string): Analysis horizon | 分析期間
- `includeMarkdown` (boolean): Include Markdown report | Markdown レポートを含む
- `includeNarrative` (boolean): Generate AI narrative summary (requires Gemini API key) | AI ナラティブ生成
- `output_mode` (string): Output verbosity. compact=TL;DR + key numbers only (default), detailed=full Markdown report | 出力詳細度。compact=主要数値のみ（デフォルト）、detailed=全文レポート付き
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `weights` (object): Custom axis weights (defaults: 0.25/0.20/0.20/0.20/0.15) | 軸の重み

Output parameters:

- `attribution` (string)
- `axes` (array): Per-axis scores with evidence
- `compositeScore` (number): Overall composite score 0-100
- `markdownReport` (string): Full Markdown report
- `narrative` (string): AI-generated executive summary (if Gemini available)
- `peerComparison` (array): Top/bottom peer cities for comparison
- `tier` (string): Tier rating: S(80+) A(65-79) B(50-64) C(<50)

### `get_zoning_info` (~152 tokens)

用途地域情報

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。

Input parameters:

- `area` (string, required): Target area (e.g. '名古屋市中区', '新宿区') | 対象エリア
- `district` (string): Specific district (e.g. '栄', '西新宿') | 地区名
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）

### `get_vacancy_stats` (~129 tokens)

空き家率統計

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。

Input parameters:

- `area` (string): Target city (e.g. '名古屋市中区') — omit for full prefecture | 対象市区町村
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）

### `get_population_outlook` (~133 tokens)

将来人口推計

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。

Input parameters:

- `area` (string): Target city (e.g. '名古屋市中区') — omit for full prefecture | 対象市区町村
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）

### `get_real_estate_macro_snapshot` (~222 tokens)

不動産マクロスナップショット

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。

Input parameters:

- `area` (string): Optional city filter (e.g. '名古屋市中区') | 市区町村で絞り込み
- `includeExternalSeries` (boolean): If true, fetch construction starts (e-Stat, needs ESTAT_APP_ID) and policy-rate proxy (FRED CSV, no key). | e-Stat着工・FRED金利系列を併記
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）

### `detect_arbitrage_signals` (~277 tokens)

価格トライアングル・アービトラージスキャン

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。

Input parameters:

- `includeLive` (boolean): Fetch latest MLIT transactions live (requires MLIT_API_KEY) | ライブ取引価格取得
- `limit` (integer): Max cities to return | 最大返却市区町村数
- `output_mode` (string): Output verbosity. compact=TL;DR + key numbers only (default), detailed=full Markdown report | 出力詳細度。compact=主要数値のみ（デフォルト）、detailed=全文レポート付き
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `signalType` (string): Filter by signal type: 'discount' | 'inheritance_edge' | 'overheated' | 'fair' | omit for all | シグナル種別フィルター

Output parameters:

- `attribution` (string)
- `benchmark` (object): 比較用ベンチマーク
- `dataYear` (number): データ年次
- `items` (array): 検出シグナル一覧
- `liveDataUsed` (boolean): MLIT ライブ取引データ使用
- `markdownReport` (string): Markdown 形式の分析レポート
- `prefecture` (string)
- `scannedCities` (number): スキャンした市区町村数

### `review_purchase_recommendation` (~702 tokens)

購入候補物件レビュー

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。

Input parameters:

- `addressMemo` (string): 販売図面・物件資料に記載された住所メモ
- `askingPrice` (number, required): 売出価格・購入打診価格（円）
- `buildingAge` (number): 築年数
- `buildingAreaSqm` (number): 建物面積（㎡）
- `city` (string, required): 市区町村（例: '名古屋市中区', '新宿区'）
- `currentAnnualRent` (number): 現況年間賃料（円）
- `district` (string): 町丁目・地区名（販売図面から分かる範囲で可）
- `exclusiveAreaSqm` (number): 専有面積（㎡）
- `expectedAnnualRent` (number): 想定年間賃料（円）
- `floors` (integer): 階数
- `kojiPricePerSqm` (number): 参考公示地価（円/㎡）
- `landAreaSqm` (number): 土地面積（㎡）
- `negotiablePrice` (number): 交渉後に狙う価格（円）
- `occupancyRate` (number): 想定稼働率（0-1）
- `operatingExpenseAnnual` (number): 年間運営費・管理費・修繕費等（円）
- `output_mode` (string): Output verbosity. compact=TL;DR + key numbers only (default), detailed=full Markdown report | 出力詳細度。compact=主要数値のみ（デフォルト）、detailed=全文レポート付き
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `propertyTaxAnnual` (number): 固定資産税等（円/年）
- `propertyType` (string): 物件種別
- `proposedTerms` (object): 提案中の契約条件
- `recommenderClaim` (string): 仲介会社・営業担当などが勧めている理由
- `renovationCost` (number): 想定リノベ/修繕費（円）
- `rosenkaPricePerSqm` (number): 参考路線価（円/㎡）
- `structure` (string): 構造（RC/SRC/S/木造など）
- `transactionMedianPerSqm` (number): 近隣取引相場中央値（円/㎡）

Output parameters:

- `attribution` (string)
- `axes` (array)
- `contractScore` (number)
- `dashboardUri` (string)
- `dataSources` (array)
- `decision` (string)
- `decisionLabel` (string)
- `futureScore` (number)
- `keyNumbers` (object)
- `markdownReport` (string)
- `missingInformation` (array)
- `negotiationPoints` (array)
- `overallScore` (number)
- `priceScore` (number)
- `recommendedClauses` (array)
- `redFlags` (array)
- `riskScore` (number)
- `yieldScore` (number)

### `simulate_leveraged_cashflow` (~555 tokens)

レバレッジ10年キャッシュフロー試算

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。

Input parameters:

- `annualCapex` (number): 毎年の資本的支出・大規模修繕積立相当（円/年）
- `annualRent` (number, required): 初年度の想定年間賃料収入（円）
- `askingPrice` (number, required): 購入価格・売出価格（円）
- `assumptions` (object): 10年収支・税務前提
- `city` (string, required): 市区町村（例: '名古屋市中区', '新宿区'）
- `district` (string): 町丁目・地区名（任意）
- `landValueRatio` (number): 土地按分比率。建物減価償却のために使用（0-1）
- `loan` (object, required): 銀行借入条件
- `operatingExpenseAnnual` (number): 管理費・修繕費・保険料など年間運営費（円）
- `otherIncomeAnnual` (number): 駐車場・看板等のその他年間収入（円）
- `output_mode` (string): Output verbosity. compact=TL;DR + key numbers only (default), detailed=full Markdown report | 出力詳細度。compact=主要数値のみ（デフォルト）、detailed=全文レポート付き
- `prefecture` (string): 都道府県名（和名/英名/ISO 3166-2 コード対応）
- `propertyTaxAnnual` (number): 固定資産税・都市計画税等（円/年）
- `propertyType` (string): 物件種別
- `purchaseCost` (number): 仲介手数料・登記費用など初期取得費用（円）
- `renovationCost` (number): 初期修繕・リノベーション費用（円）
- `vacancyRate` (number): 初年度の想定空室率（0-1）

Output parameters:

- `assumptions` (object)
- `attribution` (string)
- `city` (string)
- `dashboardUri` (string)
- `district` (string|null)
- `markdownReport` (string)
- `prefecture` (string)
- `recommendations` (array)
- `redFlags` (array)
- `sensitivity` (array)
- `summary` (string)
- `summaryKpis` (object)
- `yearlyRows` (array)

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/sugukurukabe-japan-real-estate-intel-mcp/realestate-mcp#diagnostics

## Score history

- 2026-08-03: 46
- 2026-08-02: 70
- 2026-08-01: 77
- 2026-07-31: 51
- 2026-07-30: 31
- 2026-07-29: 31
- 2026-07-28: 40
- 2026-07-27: 31
- 2026-07-26: 64

## Links

- Remote endpoint: https://realestate-mcp.jp/mcp
- Repository: https://github.com/sugukurukabe/japan-real-estate-intel-mcp
- Website: https://realestate-mcp.jp/dashboard.html
- Changelog RSS feed: https://verifymcp.io/servers/sugukurukabe-japan-real-estate-intel-mcp/realestate-mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/sugukurukabe-japan-real-estate-intel-mcp/realestate-mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/sugukurukabe-japan-real-estate-intel-mcp/realestate-mcp