此 MCP 可以做什么
Supports commercial real estate underwriting with live rates, DCF models, lease and rent-roll analysis, demographics, market data, and investment memos.
功能
工具
13
工具
abstract_lease
Extract all key terms from a commercial lease document.
Returns term, base rent schedule, escalations, TI allowance, CAM structure,
renewal options, termination rights, exclusivity, co-tenancy, and red flags.
输入模式
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'description': 'Raw text copied from a commercial lease PDF'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
analyze_rent_roll
Extract structured tenant and lease data from a rent roll document.
Paste the text content of your rent roll PDF here (copy-paste from PDF reader).
Returns tenant list, suite/SF, lease dates, monthly rent, escalations, and options.
输入模式
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'description': 'Raw text copied from a rent roll PDF'}, 'property_name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'Optional property name for context'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
build_dcf_model
Build a levered DCF model using live Federal Reserve rates.
Automatically fetches current SOFR to derive the loan rate if not provided.
Returns: annual cash flows, IRR, equity multiple, cash-on-cash, DSCR, and exit analysis.
输入模式
{'type': 'object', 'required': ['noi_year1', 'purchase_price'], 'properties': {'loan_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'default': None, 'description': 'Loan interest rate % â\x80\x94 if None, fetches live SOFR + 175bps'}, 'noi_year1': {'type': 'number', 'description': 'Year 1 Net Operating Income ($)'}, 'equity_pct': {'type': 'number', 'default': 35.0, 'description': 'Equity as % of purchase price (default 35%)'}, 'hold_years': {'type': 'integer', 'default': 10, 'description': 'Hold period in years (default 10)'}, 'exit_cap_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'default': None, 'description': 'Exit cap rate % â\x80\x94 if None, uses entry cap + 25bps (conservative)'}, 'purchase_price': {'type': 'number', 'description': 'Acquisition price ($)'}, 'noi_growth_rate': {'type': 'number', 'default': 3.0, 'description': 'Annual NOI growth rate % (default 3.0)'}, 'amortization_years': {'type': 'integer', 'default': 30, 'description': 'Loan amortization period (default 30 years)'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
export_dcf_excel
Generate a downloadable Excel (.xlsx) underwriting model with LIVE formulas â
editable assumptions, PMT/FV amortization, IRR, equity multiple, a sensitivity
grid, live Fed rates, and (if an address is given) Census trade-area demographics.
Returns a download link valid for 60 minutes.
输入模式
{'type': 'object', 'required': ['noi_year1', 'purchase_price'], 'properties': {'address': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'Optional property address â\x80\x94 adds a demographics sheet'}, 'loan_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'default': None, 'description': 'Loan rate % â\x80\x94 default live SOFR + 175bps'}, 'noi_year1': {'type': 'number', 'description': 'Year 1 Net Operating Income ($)'}, 'equity_pct': {'type': 'number', 'default': 35.0, 'description': 'Equity as % of price (default 35)'}, 'hold_years': {'type': 'integer', 'default': 10, 'description': 'Hold period (default 10)'}, 'exit_cap_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'default': None, 'description': 'Exit cap % â\x80\x94 default entry cap + 25bps'}, 'property_name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'Optional label for the model header'}, 'purchase_price': {'type': 'number', 'description': 'Acquisition price ($)'}, 'noi_growth_rate': {'type': 'number', 'default': 3.0, 'description': 'Annual NOI growth % (default 3.0)'}, 'amortization_years': {'type': 'integer', 'default': 30, 'description': 'Amortization (default 30)'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
flag_lease_risks
Analyze a parsed rent roll for investment risks.
Feed the output from analyze_rent_roll directly into this tool.
Returns: rollover risk, tenant concentration, credit risk, and actionable recommendations.
输入模式
{'type': 'object', 'required': ['rent_roll_json'], 'properties': {'rent_roll_json': {'type': 'string', 'description': 'JSON string from the analyze_rent_roll tool output'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
generate_deal_memo
Generate a formatted CRE acquisition memo / Investment Committee memo.
Automatically pulls live rates from FRED and demographics from Census Bureau
to provide real market context â not guesses.
输入模式
{'type': 'object', 'required': ['property_address', 'property_type', 'noi', 'asking_price'], 'properties': {'noi': {'type': 'number', 'description': 'Net Operating Income ($)'}, 'asking_price': {'type': 'number', 'description': 'Asking price ($)'}, 'property_type': {'type': 'string', 'description': 'Multifamily / Office / Retail / Industrial / Mixed-Use'}, 'property_address': {'type': 'string', 'description': 'Full property address'}, 'rent_roll_summary': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'Optional: paste output from analyze_rent_roll or flag_lease_risks'}, 'additional_context': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'Any additional deal notes, seller info, market color'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
get_cre_market_data
Get Commercial Real Estate price index and broader market data from the Federal Reserve.
Returns CRE price trends, office/retail/industrial vacancy proxies, and credit spreads.
Provides macro context for deal underwriting and cap rate analysis.
输入模式
{'type': 'object', 'properties': {}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
get_current_rates
Get live interest rates from the Federal Reserve (FRED).
Returns SOFR, 10-year Treasury, 5-year Treasury, Fed Funds Rate, and 30-day SOFR average.
Also calculates implied cap rate ranges based on current treasury spreads.
Use this BEFORE any DCF model or loan underwriting. These are real-time numbers
Claude cannot access on its own.
输入模式
{'type': 'object', 'properties': {}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
get_inflation_data
Get current CPI and rent inflation data from the Federal Reserve.
Returns overall inflation, shelter inflation, and rent-specific CPI with YoY changes.
Use this to calibrate rent growth assumptions in your DCF model â don't guess.
输入模式
{'type': 'object', 'properties': {}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
get_market_demographics
Get Census Bureau demographics for any US property address.
Returns median income, population, employment rate, housing vacancy,
median rents, and education levels for the census tract.
This is address-specific data from the actual Census tract â not estimates.
Claude cannot access this without the MCP.
For 1/3/5-mile trade-area rings, use get_radius_demographics instead.
输入模式
{'type': 'object', 'required': ['address'], 'properties': {'address': {'type': 'string', 'description': 'Full US property address (e.g. "1234 Main St, Charlotte, NC 28202")'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
get_radius_demographics
Get aggregated Census demographics for radius rings around a US property address â
the standard 1/3/5-mile trade-area format used in CRE site analysis.
Aggregates every census tract whose centroid falls within each radius:
population, household-weighted median income, employment rate, college attainment,
housing vacancy, renter share, and median rent.
Use this for trade-area / site analysis. Use get_market_demographics for the
single census tract immediately around the address.
输入模式
{'type': 'object', 'required': ['address'], 'properties': {'address': {'type': 'string', 'description': 'Full US property address (e.g. "1234 Main St, Charlotte, NC 28202")'}, 'radii_miles': {'type': 'string', 'default': '1,3,5', 'description': 'Comma-separated radii in miles (default "1,3,5", each capped at 15)'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
screen_land_market
Screen a US county as a LAND-INVESTING market (raw-land flip / Podolsky style).
Grades the county on the signals that matter for buying cheap rural land and
reselling on terms: population growth, demographics, owner share, and affordability.
IMPORTANT: This screens on FREE Census data only (growth + demographics + a
home-value affordability proxy). It does NOT include actual land sale prices or
comps â those require county records or a paid service, and must be verified
per-parcel before buying. Use this to rank/shortlist markets, not to buy.
输入模式
{'type': 'object', 'required': ['state', 'county'], 'properties': {'state': {'type': 'string', 'description': '2-letter state abbreviation (e.g. "AZ") or 2-digit state FIPS'}, 'county': {'type': 'string', 'description': 'County name (e.g. "Mohave" or "Mohave County")'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
screen_parcel_dd
Pre-screen a land parcel's location for the AUTOMATABLE due-diligence red flags:
FEMA flood zone and federal wetlands. Pulls live from FEMA's National Flood Hazard
Layer and the US Fish & Wildlife National Wetlands Inventory.
Use this to kill obviously-bad parcels (flood zone, wetlands) at scale BEFORE
spending time on manual due diligence.
IMPORTANT: Checks flood + wetlands only. It does NOT check legal ACCESS
(landlocked â the #1 land deal-killer), title/liens, or zoning â those stay MANUAL,
per-parcel checks via county records. A clean screen here is necessary, NOT sufficient.
输入模式
{'type': 'object', 'required': ['lat', 'lng'], 'properties': {'lat': {'type': 'number', 'description': 'Parcel latitude (decimal degrees)'}, 'lng': {'type': 'number', 'description': 'Parcel longitude (decimal degrees)'}}, 'additionalProperties': False}
输出模式
{'type': 'object', 'additionalProperties': True}
历史
近期工具变更
已添加
export_dcf_excel
2026年9月17日 12:53
已添加
generate_deal_memo
2026年9月17日 12:53
已添加
build_dcf_model
2026年9月17日 12:53
已添加
flag_lease_risks
2026年9月17日 12:53
已添加
abstract_lease
2026年9月17日 12:53
已添加
analyze_rent_roll
2026年9月17日 12:53
已添加
get_cre_market_data
2026年9月17日 12:53
已添加
get_inflation_data
2026年9月17日 12:53
已添加
screen_parcel_dd
2026年9月17日 12:53
已添加
screen_land_market
2026年9月17日 12:53
已添加
get_radius_demographics
2026年9月17日 12:53
已添加
get_market_demographics
2026年9月17日 12:53
已添加
get_current_rates
2026年9月17日 12:53
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