此 MCP 可以做什么
Analyzes U.S. homes and ZIP codes for affordability, buy-versus-rent outcomes, appreciation, rents, and neighborhood signals.
功能
工具
6
工具
affordability
Can this buyer afford this home? Returns the monthly PITI payment, front-end and
back-end debt-to-income ratios (what a lender qualifies on), the income needed at a
28% front-end ratio, estimated after-tax money left over each month, the cash cushion
left after closing, and a plain Comfortable / Qualifiable-tight / Stretch verdict.
monthly_debts = recurring debt obligations a lender counts (car, student loan, minimum
credit-card) — NOT living costs; this feeds DTI. monthly_expenses = living costs (food,
utilities, childcare) used ONLY for the 'left over each month' life check, never DTI.
Pass `address` to ground property tax + insurance in the ZIP's real rates. Pass
`liquid_savings` to get the post-closing cash cushion (months of payment covered).
只读
可访问外部资源
输入模式
{'type': 'object', 'title': 'affordabilityArguments', 'required': ['home_price', 'annual_income'], 'properties': {'address': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Address', 'default': None}, 'fixed_rate': {'type': 'number', 'title': 'Fixed Rate', 'default': 6.75}, 'home_price': {'type': 'number', 'title': 'Home Price'}, 'hoa_monthly': {'type': 'number', 'title': 'Hoa Monthly', 'default': 0.0}, 'annual_income': {'type': 'number', 'title': 'Annual Income'}, 'monthly_debts': {'type': 'number', 'title': 'Monthly Debts', 'default': 0.0}, 'insurance_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Insurance Rate', 'default': None}, 'liquid_savings': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Liquid Savings', 'default': None}, 'down_payment_pct': {'type': 'number', 'title': 'Down Payment Pct', 'default': 20.0}, 'monthly_expenses': {'type': 'number', 'title': 'Monthly Expenses', 'default': 0.0}, 'marginal_tax_rate': {'type': 'number', 'title': 'Marginal Tax Rate', 'default': 24.0}, 'property_tax_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Property Tax Rate', 'default': None}}}
affordable_price
What's the most this buyer can afford? The reverse of `affordability` — 'what price
can I afford?' instead of 'can I afford this specific home?'. Solves for the maximum
home price under standard lender limits: a front-end DTI cap (default 28% = housing /
gross income) and a back-end cap (default 36% = housing + other debts / gross income).
If `liquid_savings` is given, also caps by the cash available for down payment + closing
and reports which limit binds. Returns the affordable price, the PITI and DTIs at that
price, and the cash to close. Pass `address` to ground property tax + insurance in the
ZIP's real rates.
This is a lender-limit ceiling, not a comfort recommendation — feed the result into
`affordability` (or buy_vs_rent) to check monthly slack and whether buying pencils.
只读
可访问外部资源
输入模式
{'type': 'object', 'title': 'affordable_priceArguments', 'required': ['annual_income'], 'properties': {'address': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Address', 'default': None}, 'fixed_rate': {'type': 'number', 'title': 'Fixed Rate', 'default': 6.75}, 'hoa_monthly': {'type': 'number', 'title': 'Hoa Monthly', 'default': 0.0}, 'annual_income': {'type': 'number', 'title': 'Annual Income'}, 'monthly_debts': {'type': 'number', 'title': 'Monthly Debts', 'default': 0.0}, 'insurance_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Insurance Rate', 'default': None}, 'liquid_savings': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Liquid Savings', 'default': None}, 'back_end_dti_pct': {'type': 'number', 'title': 'Back End Dti Pct', 'default': 36.0}, 'down_payment_pct': {'type': 'number', 'title': 'Down Payment Pct', 'default': 20.0}, 'front_end_dti_pct': {'type': 'number', 'title': 'Front End Dti Pct', 'default': 28.0}, 'property_tax_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Property Tax Rate', 'default': None}}}
analyze_address
Look up a US address (or bare 5-digit ZIP): ZIP-level home-price appreciation
forecast (FHFA data back to 1975), estimated market rent, and neighborhood signals
(schools, walkability, water quality, grocery/dining/outdoors proximity).
For a full street address this also returns sub-ZIP `nbhd*` fields describing the
specific census tract - how its prices, incomes and build era compare to the rest of
the ZIP. That is POSITION, not a neighborhood forecast; see _units.
property_type: sfr | condo | townhome | multi
只读
可访问外部资源
输入模式
{'type': 'object', 'title': 'analyze_addressArguments', 'required': ['address'], 'properties': {'address': {'type': 'string', 'title': 'Address'}, 'bedrooms': {'type': 'integer', 'title': 'Bedrooms', 'default': 2}, 'property_type': {'type': 'string', 'title': 'Property Type', 'default': 'sfr'}}}
buy_vs_rent
Should someone buy this home or keep renting? Runs the TerraVue engine: a
deterministic 30-year simulation plus a 500-scenario Monte Carlo over correlated
market paths. Returns the probability buying wins, the breakeven hold period, and
net-worth outcomes.
monthly_rent = what the person would pay to rent THE SAME HOME to live in — an
owner-occupant buy-vs-rent decision, NOT rental income they'd collect as a landlord
(this tool does not model rental cash flow; that lens lives on terravue.app). A rent
far out of line with home_price is almost certainly a misunderstanding — confirm it
before trusting the verdict. If `address` is given, the ZIP's real appreciation forecast
and regional tax/insurance defaults are used (explicit parameters still win).
只读
可访问外部资源
输入模式
{'type': 'object', 'title': 'buy_vs_rentArguments', 'required': ['home_price', 'monthly_rent'], 'properties': {'address': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Address', 'default': None}, 'fixed_rate': {'type': 'number', 'title': 'Fixed Rate', 'default': 6.75}, 'home_price': {'type': 'number', 'title': 'Home Price'}, 'hoa_monthly': {'type': 'number', 'title': 'Hoa Monthly', 'default': 0.0}, 'monthly_rent': {'type': 'number', 'title': 'Monthly Rent'}, 'analysis_years': {'type': 'integer', 'title': 'Analysis Years', 'default': 30}, 'down_payment_pct': {'type': 'number', 'title': 'Down Payment Pct', 'default': 20.0}, 'property_tax_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Property Tax Rate', 'default': None}, 'home_appreciation_rate': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'title': 'Home Appreciation Rate', 'default': None}, 'investment_return_rate': {'type': 'number', 'title': 'Investment Return Rate', 'default': 9.5}}}
compare_areas
Analyze and RANK 2-12 ZIPs or addresses side by side on the signals TerraVue has:
home-price appreciation forecast (and how each compares to the national average),
school rating, walkability, education level, and water quality. Returns the correct
city name per ZIP, so locations are never guessed or mislabeled.
Use for "which areas have the best appreciation / schools / upside." TerraVue has no
listing inventory and cannot discover ZIPs on its own — to scan a metro, pass that
metro's ZIP codes (the ranking then covers exactly what you passed).
只读
可访问外部资源
输入模式
{'type': 'object', 'title': 'compare_areasArguments', 'required': ['addresses'], 'properties': {'bedrooms': {'type': 'integer', 'title': 'Bedrooms', 'default': 2}, 'addresses': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Addresses'}}}
request_capability
Record a question TerraVue could not answer, so it can be built. Call this whenever
a user asks for something outside the current tools' coverage (rental metrics, Airbnb
modeling, valuations, move-vs-stay, non-US, anything else), then tell the user their
request was captured. Keep `description` to the capability needed — no names, emails,
or other personal details.
category: rental_metrics | str_airbnb | valuation | move_vs_stay | data_coverage | other
输入模式
{'type': 'object', 'title': 'request_capabilityArguments', 'required': ['description'], 'properties': {'category': {'type': 'string', 'title': 'Category', 'default': 'other'}, 'description': {'type': 'string', 'title': 'Description'}}}
历史
近期工具变更
已添加
affordable_price
2026年9月21日 02:40
已添加
compare_areas
2026年9月21日 02:40
已添加
affordability
2026年9月21日 02:40
已添加
buy_vs_rent
2026年9月21日 02:40
已添加
request_capability
2026年9月21日 02:40
已添加
analyze_address
2026年9月21日 02:40
发现
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