Access FUDOSAN DB directly from AI agents
MCP (Model Context Protocol) is a standard protocol for AI agents to connect to external data sources. From MCP-capable clients such as Claude.ai, ChatGPT, Claude Desktop, and Cursor, you can query FUDOSAN DB's real-estate data in natural language.
Ask questions like "What's the condo market in Shibuya?" or "Estimate the rent for this unit" — data retrieval, analysis, and rent estimation all run from one prompt.
Claude.ai and ChatGPT work with Google-account authentication only (no API key required). Other clients need an API key.
Create an account on the API key page to issue a key. Free plan is available.
Requires Node.js 18+. npx auto-installs it — no prior install needed.
Add the following to claude_desktop_config.json.
Save the config and restart Claude Desktop to activate the MCP connection. FUDOSAN DB will appear in the tool list.
Search municipalities by prefecture / city name. Returns area profiles (transaction count, median, land price, etc.).
Get detailed area profile by municipality code.
Transaction price trends for a given area. Filterable by property type.
Public land price trends for a given area (annual).
Rankings by metric: transaction count, median price, land price, etc.
List all municipalities. Filterable by prefecture.
AI rent estimation from area, size, age, layout, etc. Accuracy (MAPE), measured under this tool's own input conditions on properties unseen during training (measured 2026-09-05, v5.3-areafix, duplicate rows excluded): approx. 10.7% (9 urban prefectures) / approx. 10.1% (38 rural prefectures) with no optional fields supplied, improving to approx. 9.4% (urban) / 9.5% (rural) with station_name alone, and approx. 9.2% when both station_name and category are supplied. Estimates for 88 m² and above are outside the range covered by the accuracy figures above because that band has few training listings (the response carries a warning and confidence.level="low"; treat as reference values). Accuracy figures for the 88 m²+ band are being re-measured after the September 2026 retraining.
Real-estate investment cash-flow simulation. Yield / CF / IRR for up to 10 years.
Forecast municipality-level rent change (1y / 3y). 22 features across macro indicators, future population, and regional characteristics. MAE 1.6pp, direction accuracy 83%.
Future rent forecast. Phase 2 rent estimate × Phase 3 change rate. Starting point for medium-term yield analysis.
Cross-REIT property search. Filter by area, use type, cap-rate range. 6,900+ properties / 73 REITs.
Per-property period history (occupancy / rent / appraisal / cap rate), up to 10 periods.
Per-REIT portfolio summary (AUM, avg cap rate, occupancy).
J-REIT disposition events: sale price, book value, gain/loss, price per tsubo, sponsor-transaction flag.
J-REIT acquisition events (unified transaction-case format). Acquisition date / price / price per tsubo / seller / appraisal / NOI cap / sponsor-transaction / holding years.
Hotel-property monthly operations (ADR / RevPAR / GOP / occupancy, JPY and USD).
Periodic appraisal details (direct-cap price / DCF / discount rate / terminal cap rate / appraisal NOI).
Cap rate evaluation bias by appraiser. Deviation from peer median aggregated by use_type × region × year. Positive = conservative, negative = aggressive.
Early-warning alerts on appraisal declines. Period-over-period drops of -5%+ or cap rate spikes of +0.3pp classified as severe / alert / watch. For activist / institutional investor screening.
Per-REIT future population risk aggregated from 250m-mesh XKT013 (2020-2050) × geocoded REIT property addresses. Includes acquisition-price-weighted weighted_pop_change_2050, pct_high_plus, and pct_aum_high_risk to quantify demographic decline exposure.
Top-tenant search per property: tenant name, industry, leased area, contract form. Anonymized tenants included.
J-REIT × sponsor-company master. Reference data for sponsor-transaction detection.
Normalized master of sellers / buyers / SPCs. Useful for deduplication and identifying major buyers/sellers.
Non-REIT corporate real-estate transaction cases. LLM-structured extraction from EDINET annual-report facility plans and sale notes. Filterable by transaction type, amount, counterparty.
Sponsor consolidated financials × REIT disposition, by fiscal year. Useful for "properties concentrated during a sponsor-decline year" analysis.
Calibrated probability score (0-100) for sponsor → REIT transfers next fiscal year. Calibrated LR v1 / Leave-One-Sponsor-Out AUC=0.75. Per-feature contribution (log-odds) and confidence label (high/medium/low) included.
Listed-company real-estate holdings × land price × 2050 population forecast × hazard risk. 267K facilities across 4,322 companies.
Hazard-zone aggregation in the municipality where each J-REIT property sits (steep slope / landslide / tsunami / flood / storm surge). Values are municipality-level counts, not a parcel-level overlay, and do not indicate whether the property's parcel intersects a zone (properties in the same municipality share the same values).
Prompts you can drop straight into an MCP-connected Claude. Copy and run as-is.
The same MCP connection works from Claude Code. Add the config to ~/.claude/settings.json.