japan-real-estate-intel (sugukurukabe/japan-real-estate-intel-mcp) is an MCP server listed on the M8ven Trust Index. It scores 56 out of 100, grade D. It declares 24 tools. No publisher has claimed this listing.

D
Caution
56/100

japan-real-estate-intel

| 地価トレンド予測 | 新宿区の5年後地価をAI予測。CAGR・投資シグナル付き | | 企業立地需要分析 | 名古屋市中区のオフィス・工場需要スコアを算出 | | ファミリー向け適性評価 | 横浜市西区の教育・安全・医療スコアを総合評価 | | ポートフォリオ最適化 | 東京・大阪・埼玉の3エリアに投資配分を最適化 | | What-If シナリオ分析 | 大阪市中央区で新駅開設シナリオを試算 | | 店舗出店適地評価 | 福岡市博多区の人流・商業施設・交通データで出店適性を判定 |

Caution. Specific findings reduced this grade. They are listed on the page. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

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Who stands behind it

sugukurukabe

Source: Glama

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Install from

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings
⚠️
Known vulnerabilities in dependencies: 5 high
Affects packages this MCP installs at runtime. Upgrade or remove the affected dependency.
24 tools verified — handlers match their declared behaviour
24 read-only tools verified — handlers contain no write/delete/exec
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
🔐
You'll be asked for 8 credentials: API_KEY, GOOGLE_GENAI_API_KEY, GOOGLE_MAPS_API_KEY, LICENSE_KEY, METRICS_KEY, MLIT_API_KEY, STRIPE_SECRET_KEY, STRIPE_WEBHOOK_SECRET
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// tools this server exposes24 tools

These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.

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。

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。

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。

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。

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。

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。

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。

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。

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。

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_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。

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。

assess_exterior_visuals

AI visual exterior audit of a property using the Google Maps Street View Static API (https://developers.google.com/maps/documentation/streetview) and Google Gemini Vision (https://ai.google.dev/gemini-api/docs/vision) for image analysis. Falls back to a simulated audit if GOOGLE_MAPS_API_KEY/GOOGLE_

analyze_commute_accessibility

Transit commute accessibility analyzer to regional station hubs, using the Google Maps Distance Matrix API (https://developers.google.com/maps/documentation/distance-matrix). Calculates travel times, routes, and overall score. | 交通通勤アクセシビリティ評価。Google Maps Distance Matrix APIで主要ターミナル駅への所要時間・経路・利便性スコア

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。

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。

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。

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。

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。

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。

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。

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。

generate_contract_support_package

Contract support package: generate risk matrix, price negotiation anchors, recommended clauses from neighborhood/property data. Returns a downloadable Markdown report via resource_link. | 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。Markdownレポートをresource_linkとしてダウンロード可能。

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。

// known CVEs in dependencies5 high1 low

Disclosed vulnerabilities in this server's declared npm dependencies (via OSV). Whether each is reachable depends on the installed versions.

high@modelcontextprotocol/sdk@1.12.1GHSA-345p-7cg4-v4c7

@modelcontextprotocol/sdk has cross-client data leak via shared server/transport instance reuse

high@modelcontextprotocol/sdk@1.12.1GHSA-8r9q-7v3j-jr4g

Anthropic's MCP TypeScript SDK has a ReDoS vulnerability

high@modelcontextprotocol/sdk@1.12.1GHSA-w48q-cv73-mx4w

Model Context Protocol (MCP) TypeScript SDK does not enable DNS rebinding protection by default

highxlsx@0.18.5GHSA-4r6h-8v6p-xvw6

Prototype Pollution in sheetJS

highxlsx@0.18.5GHSA-5pgg-2g8v-p4x9

SheetJS Regular Expression Denial of Service (ReDoS)

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// environment variables
To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.
🔐 secretAPI_KEY~50 tool calls / month (UTC), applies to both the public https://realestate-mcp.jp connector (per MCP session) and self-hosted stdio (per client) — see TIER_MONTHLY_TOOL_CALLS. Pro/Enterprise unlock via license key, no relation to API_KEY
configARTIFACT_DB_PATH
configARTIFACT_DIR
configARTIFACT_TTL_HOURS
configBQ_DATASET
configDEFAULT_TIER
configGCP_PROJECT
🔐 secretGOOGLE_GENAI_API_KEY
🔐 secretGOOGLE_MAPS_API_KEY
configHOST
configLICENSE_DB_PATH
🔐 secretLICENSE_KEY
configLICENSE_PRIVATE_KEY_PEM
configLICENSE_SERVER_URL
configLOG_LEVEL
configMCP_PUBLIC_URL
🔐 secretMETRICS_KEY
🔐 secretMLIT_API_KEYRun npm run data:fetch (requires MLIT_API_KEY / ESTAT_APP_ID for live sources) — recommend quarterly refresh for production
configRATE_LIMIT_ENABLED
configRATE_LIMIT_MAX
configRATE_LIMIT_WINDOW_MS
configSESSION_TIMEOUT_MS
🔐 secretSTRIPE_SECRET_KEY
🔐 secretSTRIPE_WEBHOOK_SECRET
configTIER_MONTHLY_TOOL_CALLS~50 tool calls / month (UTC), applies to both the public https://realestate-mcp.jp connector (per MCP session) and self-hosted stdio (per client) — see TIER_MONTHLY_TOOL_CALLS. Pro/Enterprise unlock via license key, no relation to API_KEY
configUSAGE_CLIENT_ID
configUSAGE_DB_PATH
Deployment configuration, supplied by whoever hosts the server. Users are not asked for these.
deployPORT
deploySENTRY_DSN
// quality suggestions

Production dependencies are patched

0 critical, 5 high severity in production deps — @modelcontextprotocol/sdk@1.12.1 (high), @modelcontextprotocol/sdk@1.12.1 (high)

Run npm audit fix, or upgrade the affected packages to a non-vulnerable version.

Dependency freshness

1/20 production deps stale: topojson-client@2022-06-27 (4.2y)

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 1 concrete improvement we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
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commit: 7fec477dd75e7b5a24179aef4f7ca14ebe1107b3
code hash: 2a5e77e6d71283ded7ffd175eb30375fb8cc6acd6db13a719798bab10ca7d863
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