""" Real Estate Tool — AgentForge integration ========================================== Feature flag: set ENABLE_REAL_ESTATE=true in .env to activate. When the flag is absent or false, all functions return a disabled stub and the graph never routes queries here. Three capabilities: 1. search_listings(query) — find homes by city/zip/neighborhood 2. get_neighborhood_snapshot(location) — market stats for an area 3. get_listing_details(listing_id) — full detail for one listing Provider strategy: - MockProvider (default, always safe): realistic sample data for 10 US cities. Works offline, zero latency, no API key required. - Real provider (future drop-in): swap _PROVIDER to "attom" or "rapidapi" and set REAL_ESTATE_API_KEY. The normalize schema is identical. Data schema (NormalizedListing): id, address, city, state, zip, price, bedrooms, bathrooms, sqft, price_per_sqft, days_on_market, listing_type, status, year_built, hoa_monthly, estimated_monthly_rent, cap_rate_estimate, description Data schema (NeighborhoodSnapshot): city, state, median_price, price_per_sqft, median_dom, price_change_yoy_pct, inventory_level, walk_score, listings_count, rent_to_price_ratio, market_summary """ import os import time from datetime import datetime # --------------------------------------------------------------------------- # Feature flag # --------------------------------------------------------------------------- def is_real_estate_enabled() -> bool: """Returns True only when ENABLE_REAL_ESTATE=true in environment.""" return os.getenv("ENABLE_REAL_ESTATE", "false").strip().lower() == "true" _FEATURE_DISABLED_RESPONSE = { "tool_name": "real_estate", "success": False, "tool_result_id": "real_estate_disabled", "error": { "code": "REAL_ESTATE_FEATURE_DISABLED", "message": ( "The Real Estate feature is not currently enabled. " "Set ENABLE_REAL_ESTATE=true in your environment to activate it." ), }, } # --------------------------------------------------------------------------- # In-memory TTL cache (5-minute TTL, safe for a single-process server) # --------------------------------------------------------------------------- _cache: dict[str, dict] = {} _CACHE_TTL_SECONDS = 300 def _cache_get(key: str) -> dict | None: entry = _cache.get(key) if entry and (time.time() - entry["ts"]) < _CACHE_TTL_SECONDS: return entry["data"] return None def _cache_set(key: str, data: dict) -> None: _cache[key] = {"ts": time.time(), "data": data} def cache_clear() -> None: """Clears the entire in-memory cache. Used in tests.""" _cache.clear() # --------------------------------------------------------------------------- # Invocation logging (in-memory, no sensitive data stored) # --------------------------------------------------------------------------- _invocation_log: list[dict] = [] _MAX_LOG_ENTRIES = 500 # prevent unbounded growth def _log_invocation( function: str, query: str, duration_ms: float, success: bool, ) -> None: """ Records a single tool call to the in-memory log. query is truncated to 80 chars — no sensitive data stored. """ entry = { "timestamp": datetime.utcnow().isoformat(), "function": function, "query": query[:80], "duration_ms": round(duration_ms, 1), "success": success, } _invocation_log.append(entry) # Keep log size bounded if len(_invocation_log) > _MAX_LOG_ENTRIES: del _invocation_log[: len(_invocation_log) - _MAX_LOG_ENTRIES] def get_invocation_log() -> list[dict]: """Returns a copy of the invocation log. Called by the /real-estate/log endpoint.""" return list(_invocation_log) # --------------------------------------------------------------------------- # Mock data — realistic 2024 US market data for 10 metros # --------------------------------------------------------------------------- _MOCK_SNAPSHOTS: dict[str, dict] = { "austin": { "city": "Austin", "state": "TX", "median_price": 485_000, "price_per_sqft": 285, "median_dom": 24, "price_change_yoy_pct": -3.2, "inventory_level": "low", "walk_score": 48, "listings_count": 1_847, "rent_to_price_ratio": 0.48, "market_summary": ( "Austin remains a seller's market with limited inventory. " "Prices have cooled slightly YoY (-3.2%) after the pandemic spike, " "creating buying opportunities for long-term investors. " "Tech sector concentration adds income stability to the renter pool." ), }, "san francisco": { "city": "San Francisco", "state": "CA", "median_price": 1_250_000, "price_per_sqft": 980, "median_dom": 18, "price_change_yoy_pct": -5.8, "inventory_level": "very low", "walk_score": 88, "listings_count": 612, "rent_to_price_ratio": 0.33, "market_summary": ( "San Francisco has seen significant price correction (-5.8% YoY) " "driven by remote-work migration. Very low inventory keeps prices " "elevated despite demand softening. High rental demand from remaining " "tech workforce supports rental yields." ), }, "new york": { "city": "New York", "state": "NY", "median_price": 750_000, "price_per_sqft": 820, "median_dom": 31, "price_change_yoy_pct": 1.4, "inventory_level": "moderate", "walk_score": 95, "listings_count": 4_200, "rent_to_price_ratio": 0.52, "market_summary": ( "NYC market shows resilience with modest 1.4% YoY appreciation. " "Moderate inventory gives buyers more negotiating power than 2021–2022. " "Strong rental demand across all boroughs supports investor ROI. " "High walkability (95) is a key demand driver." ), }, "denver": { "city": "Denver", "state": "CO", "median_price": 520_000, "price_per_sqft": 310, "median_dom": 19, "price_change_yoy_pct": -1.7, "inventory_level": "low", "walk_score": 60, "listings_count": 2_100, "rent_to_price_ratio": 0.46, "market_summary": ( "Denver market stabilizing after rapid appreciation. " "Slight YoY decline (-1.7%) brings affordability back into range. " "Strong job market in tech and healthcare supports buyer demand. " "Low inventory keeps days-on-market competitive at 19 days." ), }, "seattle": { "city": "Seattle", "state": "WA", "median_price": 780_000, "price_per_sqft": 490, "median_dom": 14, "price_change_yoy_pct": 2.1, "inventory_level": "very low", "walk_score": 73, "listings_count": 890, "rent_to_price_ratio": 0.38, "market_summary": ( "Seattle is one of the tightest markets nationally, averaging just " "14 days on market. Amazon and Microsoft campuses sustain strong " "demand. Prices ticked up 2.1% YoY. Very low inventory means " "buyers face competition and often waive contingencies." ), }, "miami": { "city": "Miami", "state": "FL", "median_price": 620_000, "price_per_sqft": 425, "median_dom": 38, "price_change_yoy_pct": 4.3, "inventory_level": "moderate", "walk_score": 62, "listings_count": 3_540, "rent_to_price_ratio": 0.55, "market_summary": ( "Miami continues to attract domestic migration from high-tax states, " "pushing prices up 4.3% YoY — one of the strongest gains in the US. " "Rising insurance costs are a headwind for buyers. " "Strong Airbnb and short-term rental demand boosts investor returns." ), }, "chicago": { "city": "Chicago", "state": "IL", "median_price": 310_000, "price_per_sqft": 195, "median_dom": 28, "price_change_yoy_pct": 0.8, "inventory_level": "moderate", "walk_score": 78, "listings_count": 5_100, "rent_to_price_ratio": 0.68, "market_summary": ( "Chicago offers strong cash-flow potential with the highest " "rent-to-price ratio (0.68%) of major metros. Stable pricing " "with modest 0.8% YoY appreciation. Property taxes are a key " "consideration for investors — factor 2–3% of home value annually." ), }, "phoenix": { "city": "Phoenix", "state": "AZ", "median_price": 415_000, "price_per_sqft": 240, "median_dom": 32, "price_change_yoy_pct": -2.1, "inventory_level": "high", "walk_score": 41, "listings_count": 6_200, "rent_to_price_ratio": 0.50, "market_summary": ( "Phoenix is a buyer's market with the highest inventory of major metros. " "Prices down 2.1% YoY after the post-pandemic boom. " "Longer days on market (32) gives buyers negotiating leverage. " "Strong population growth from CA migration supports long-term demand." ), }, "nashville": { "city": "Nashville", "state": "TN", "median_price": 450_000, "price_per_sqft": 265, "median_dom": 21, "price_change_yoy_pct": 1.2, "inventory_level": "low", "walk_score": 32, "listings_count": 1_650, "rent_to_price_ratio": 0.49, "market_summary": ( "Nashville is a fast-growing Sun Belt market with strong employment " "from healthcare, tech, and entertainment sectors. Low inventory and " "short DOM (21 days) reflect healthy demand. " "No state income tax makes it attractive for relocators." ), }, "dallas": { "city": "Dallas", "state": "TX", "median_price": 395_000, "price_per_sqft": 215, "median_dom": 27, "price_change_yoy_pct": -0.5, "inventory_level": "moderate", "walk_score": 37, "listings_count": 4_800, "rent_to_price_ratio": 0.53, "market_summary": ( "Dallas-Fort Worth offers solid value with near-flat YoY pricing. " "Large inventory gives buyers choices without the frenzy of 2021–2022. " "Corporate relocations (Goldman Sachs, Oracle, HP) provide long-term " "demand foundation. No state income tax is a major draw." ), }, } _MOCK_LISTINGS: dict[str, list[dict]] = { "austin": [ { "id": "atx-001", "address": "2847 Barton Hills Dr", "city": "Austin", "state": "TX", "zip": "78704", "price": 525_000, "bedrooms": 3, "bathrooms": 2.0, "sqft": 1_850, "price_per_sqft": 284, "days_on_market": 12, "listing_type": "Single Family", "status": "Active", "year_built": 2018, "hoa_monthly": None, "estimated_monthly_rent": 2_800, "cap_rate_estimate": 4.8, "description": "Modern craftsman in sought-after 78704. Open floor plan, chef's kitchen, private backyard.", }, { "id": "atx-002", "address": "5120 Mueller Blvd #403", "city": "Austin", "state": "TX", "zip": "78723", "price": 389_000, "bedrooms": 2, "bathrooms": 2.0, "sqft": 1_100, "price_per_sqft": 354, "days_on_market": 34, "listing_type": "Condo", "status": "Active", "year_built": 2021, "hoa_monthly": 285, "estimated_monthly_rent": 2_200, "cap_rate_estimate": 4.2, "description": "Luxury condo in Mueller district. Rooftop deck, concierge, walkable to restaurants.", }, { "id": "atx-003", "address": "3901 Govalle Ave", "city": "Austin", "state": "TX", "zip": "78702", "price": 595_000, "bedrooms": 4, "bathrooms": 3.0, "sqft": 2_200, "price_per_sqft": 270, "days_on_market": 7, "listing_type": "Single Family", "status": "Active", "year_built": 2016, "hoa_monthly": None, "estimated_monthly_rent": 3_200, "cap_rate_estimate": 5.0, "description": "Spacious east Austin home. ADU potential, mature trees, 5 min from downtown.", }, { "id": "atx-004", "address": "1204 W 6th St #8", "city": "Austin", "state": "TX", "zip": "78703", "price": 699_000, "bedrooms": 3, "bathrooms": 2.5, "sqft": 1_950, "price_per_sqft": 358, "days_on_market": 19, "listing_type": "Townhouse", "status": "Active", "year_built": 2020, "hoa_monthly": 175, "estimated_monthly_rent": 3_600, "cap_rate_estimate": 4.5, "description": "Premium Clarksville townhome. Rooftop terrace with downtown skyline views.", }, { "id": "atx-005", "address": "7824 Manchaca Rd", "city": "Austin", "state": "TX", "zip": "78745", "price": 349_000, "bedrooms": 3, "bathrooms": 2.0, "sqft": 1_450, "price_per_sqft": 241, "days_on_market": 42, "listing_type": "Single Family", "status": "Price Reduced", "year_built": 2003, "hoa_monthly": None, "estimated_monthly_rent": 2_100, "cap_rate_estimate": 5.4, "description": "Best value in South Austin. Newly renovated kitchen, large yard, no HOA.", }, ], "san francisco": [ { "id": "sfo-001", "address": "1847 Castro St", "city": "San Francisco", "state": "CA", "zip": "94114", "price": 1_450_000, "bedrooms": 3, "bathrooms": 2.0, "sqft": 1_600, "price_per_sqft": 906, "days_on_market": 9, "listing_type": "Single Family", "status": "Active", "year_built": 1924, "hoa_monthly": None, "estimated_monthly_rent": 5_200, "cap_rate_estimate": 3.4, "description": "Classic Victorian in the Castro. Period details preserved, updated kitchen and baths.", }, { "id": "sfo-002", "address": "488 Folsom St #2105", "city": "San Francisco", "state": "CA", "zip": "94105", "price": 1_100_000, "bedrooms": 2, "bathrooms": 2.0, "sqft": 1_050, "price_per_sqft": 1_048, "days_on_market": 22, "listing_type": "Condo", "status": "Active", "year_built": 2018, "hoa_monthly": 890, "estimated_monthly_rent": 4_800, "cap_rate_estimate": 3.2, "description": "Luxury high-rise with bay views. Full-service building, concierge, parking included.", }, { "id": "sfo-003", "address": "222 Dolores St #7", "city": "San Francisco", "state": "CA", "zip": "94103", "price": 875_000, "bedrooms": 1, "bathrooms": 1.0, "sqft": 780, "price_per_sqft": 1_122, "days_on_market": 14, "listing_type": "Condo", "status": "Active", "year_built": 2015, "hoa_monthly": 620, "estimated_monthly_rent": 3_600, "cap_rate_estimate": 3.0, "description": "Designer Mission condo. Chef's kitchen, private patio, storage included.", }, ], "new york": [ { "id": "nyc-001", "address": "200 Water St #8B", "city": "New York", "state": "NY", "zip": "10038", "price": 895_000, "bedrooms": 2, "bathrooms": 2.0, "sqft": 1_100, "price_per_sqft": 814, "days_on_market": 18, "listing_type": "Condo", "status": "Active", "year_built": 2006, "hoa_monthly": 1_240, "estimated_monthly_rent": 5_800, "cap_rate_estimate": 4.6, "description": "FiDi condo with East River views. Doorman, gym, roof deck. Minutes from Wall St.", }, { "id": "nyc-002", "address": "78 N 7th St #4D", "city": "Brooklyn", "state": "NY", "zip": "11249", "price": 1_100_000, "bedrooms": 3, "bathrooms": 2.0, "sqft": 1_350, "price_per_sqft": 815, "days_on_market": 25, "listing_type": "Condo", "status": "Active", "year_built": 2019, "hoa_monthly": 780, "estimated_monthly_rent": 5_500, "cap_rate_estimate": 4.2, "description": "Williamsburg luxury condo. Industrial chic design, private outdoor space.", }, { "id": "nyc-003", "address": "310 W 55th St #7C", "city": "New York", "state": "NY", "zip": "10019", "price": 649_000, "bedrooms": 1, "bathrooms": 1.0, "sqft": 650, "price_per_sqft": 998, "days_on_market": 31, "listing_type": "Coop", "status": "Active", "year_built": 1967, "hoa_monthly": 1_450, "estimated_monthly_rent": 3_800, "cap_rate_estimate": 3.5, "description": "Classic midtown co-op. Full-service white glove building, 4 blocks from Central Park.", }, ], "denver": [ { "id": "den-001", "address": "2345 Larimer St #601", "city": "Denver", "state": "CO", "zip": "80205", "price": 545_000, "bedrooms": 2, "bathrooms": 2.0, "sqft": 1_400, "price_per_sqft": 389, "days_on_market": 11, "listing_type": "Condo", "status": "Active", "year_built": 2017, "hoa_monthly": 340, "estimated_monthly_rent": 2_600, "cap_rate_estimate": 4.3, "description": "RiNo district condo. Exposed brick, mountain views, walkable to food & art scene.", }, { "id": "den-002", "address": "4812 W 32nd Ave", "city": "Denver", "state": "CO", "zip": "80212", "price": 698_000, "bedrooms": 4, "bathrooms": 3.0, "sqft": 2_400, "price_per_sqft": 291, "days_on_market": 17, "listing_type": "Single Family", "status": "Active", "year_built": 2015, "hoa_monthly": None, "estimated_monthly_rent": 3_400, "cap_rate_estimate": 4.8, "description": "Highland neighborhood gem. Chef's kitchen, finished basement, large backyard deck.", }, ], "seattle": [ { "id": "sea-001", "address": "1417 NW 63rd St", "city": "Seattle", "state": "WA", "zip": "98107", "price": 895_000, "bedrooms": 3, "bathrooms": 2.0, "sqft": 1_750, "price_per_sqft": 511, "days_on_market": 8, "listing_type": "Single Family", "status": "Active", "year_built": 2014, "hoa_monthly": None, "estimated_monthly_rent": 3_800, "cap_rate_estimate": 4.1, "description": "Ballard Craftsman with Puget Sound views. Eco-smart systems, attached garage.", }, { "id": "sea-002", "address": "220 2nd Ave S #1102", "city": "Seattle", "state": "WA", "zip": "98104", "price": 699_000, "bedrooms": 2, "bathrooms": 2.0, "sqft": 1_200, "price_per_sqft": 583, "days_on_market": 13, "listing_type": "Condo", "status": "Active", "year_built": 2020, "hoa_monthly": 595, "estimated_monthly_rent": 3_200, "cap_rate_estimate": 3.9, "description": "Pioneer Square luxury condo. Amazon HQ walking distance, Elliott Bay views.", }, ], "miami": [ { "id": "mia-001", "address": "1600 Brickell Ave #3204", "city": "Miami", "state": "FL", "zip": "33129", "price": 1_200_000, "bedrooms": 3, "bathrooms": 3.0, "sqft": 2_100, "price_per_sqft": 571, "days_on_market": 22, "listing_type": "Condo", "status": "Active", "year_built": 2022, "hoa_monthly": 1_850, "estimated_monthly_rent": 7_500, "cap_rate_estimate": 5.2, "description": "Brickell ultra-luxury unit. Bayfront views, private balcony, 5-star amenities.", }, { "id": "mia-002", "address": "355 NE 1st Ave #712", "city": "Miami", "state": "FL", "zip": "33132", "price": 435_000, "bedrooms": 1, "bathrooms": 1.0, "sqft": 780, "price_per_sqft": 558, "days_on_market": 40, "listing_type": "Condo", "status": "Active", "year_built": 2014, "hoa_monthly": 680, "estimated_monthly_rent": 2_800, "cap_rate_estimate": 4.8, "description": "Downtown Miami studio + den. Airbnb-allowed building, strong short-term rental income.", }, ], "chicago": [ { "id": "chi-001", "address": "900 N Michigan Ave #2400", "city": "Chicago", "state": "IL", "zip": "60611", "price": 625_000, "bedrooms": 2, "bathrooms": 2.0, "sqft": 1_800, "price_per_sqft": 347, "days_on_market": 29, "listing_type": "Condo", "status": "Active", "year_built": 1991, "hoa_monthly": 980, "estimated_monthly_rent": 4_200, "cap_rate_estimate": 5.8, "description": "Magnificent Mile full-floor unit. Lake Michigan views, white glove service.", }, { "id": "chi-002", "address": "2140 N Damen Ave", "city": "Chicago", "state": "IL", "zip": "60647", "price": 485_000, "bedrooms": 3, "bathrooms": 2.5, "sqft": 2_100, "price_per_sqft": 231, "days_on_market": 20, "listing_type": "Single Family", "status": "Active", "year_built": 2008, "hoa_monthly": None, "estimated_monthly_rent": 3_200, "cap_rate_estimate": 6.2, "description": "Bucktown greystone townhome. Finished basement, private garage, top-rated schools.", }, ], "phoenix": [ { "id": "phx-001", "address": "4820 E Camelback Rd", "city": "Phoenix", "state": "AZ", "zip": "85018", "price": 625_000, "bedrooms": 4, "bathrooms": 3.0, "sqft": 2_800, "price_per_sqft": 223, "days_on_market": 38, "listing_type": "Single Family", "status": "Price Reduced", "year_built": 2005, "hoa_monthly": 95, "estimated_monthly_rent": 3_200, "cap_rate_estimate": 4.9, "description": "Arcadia location with Camelback Mountain views. Pool, 3-car garage, gourmet kitchen.", }, ], "nashville": [ { "id": "nas-001", "address": "600 12th Ave S #405", "city": "Nashville", "state": "TN", "zip": "37203", "price": 489_000, "bedrooms": 2, "bathrooms": 2.0, "sqft": 1_350, "price_per_sqft": 362, "days_on_market": 15, "listing_type": "Condo", "status": "Active", "year_built": 2020, "hoa_monthly": 320, "estimated_monthly_rent": 2_800, "cap_rate_estimate": 5.1, "description": "The Gulch walkable condo. No-state-income-tax advantage, steps to Broadway.", }, ], "dallas": [ { "id": "dfw-001", "address": "3421 McKinney Ave #207", "city": "Dallas", "state": "TX", "zip": "75204", "price": 389_000, "bedrooms": 2, "bathrooms": 2.0, "sqft": 1_200, "price_per_sqft": 324, "days_on_market": 21, "listing_type": "Condo", "status": "Active", "year_built": 2019, "hoa_monthly": 290, "estimated_monthly_rent": 2_400, "cap_rate_estimate": 5.4, "description": "Uptown Dallas condo. Pet-friendly, resort-style amenities, walkable to dining.", }, ], } def _normalize_city(location: str) -> str: """Maps query string to a canonical city key in mock data.""" loc = location.lower().strip() mapping = { "atx": "austin", "austin tx": "austin", "austin, tx": "austin", "sf": "san francisco", "sfo": "san francisco", "san francisco ca": "san francisco", "nyc": "new york", "new york city": "new york", "manhattan": "new york", "brooklyn": "new york", "denver co": "denver", "denver, co": "denver", "seattle wa": "seattle", "seattle, wa": "seattle", "miami fl": "miami", "miami, fl": "miami", "chicago il": "chicago", "chicago, il": "chicago", "phoenix az": "phoenix", "phoenix, az": "phoenix", "nashville tn": "nashville", "nashville, tn": "nashville", "dallas tx": "dallas", "dallas, tx": "dallas", "dfw": "dallas", } if loc in mapping: return mapping[loc] for city_key in _MOCK_SNAPSHOTS: if city_key in loc: return city_key return "" # --------------------------------------------------------------------------- # Public tool functions — all follow the standard tool result schema # --------------------------------------------------------------------------- async def get_neighborhood_snapshot(location: str) -> dict: """ Returns market-level stats for a city or neighborhood. Covers: median price, DOM, YoY change, inventory level, walk score, rent-to-price ratio, market summary. """ if not is_real_estate_enabled(): return _FEATURE_DISABLED_RESPONSE location = location.strip() tool_result_id = f"re_snapshot_{location.lower().replace(' ', '_')}_{int(datetime.utcnow().timestamp())}" _start = time.time() cache_key = f"snapshot:{location.lower()}" cached = _cache_get(cache_key) if cached: _log_invocation("get_neighborhood_snapshot", location, (time.time() - _start) * 1000, True) return cached city_key = _normalize_city(location) snap = _MOCK_SNAPSHOTS.get(city_key) if not snap: result = { "tool_name": "real_estate", "success": False, "tool_result_id": tool_result_id, "error": { "code": "REAL_ESTATE_PROVIDER_UNAVAILABLE", "message": ( f"No data found for '{location}'. " f"Supported cities: {', '.join(c.title() for c in _MOCK_SNAPSHOTS)}." ), }, } _log_invocation("get_neighborhood_snapshot", location, (time.time() - _start) * 1000, False) return result monthly_rent_estimate = round(snap["median_price"] * snap["rent_to_price_ratio"] / 100, 0) gross_yield = round(snap["rent_to_price_ratio"] * 12 / 100 * 100, 2) result = { "tool_name": "real_estate", "success": True, "tool_result_id": tool_result_id, "timestamp": datetime.utcnow().isoformat(), "result": { "location": f"{snap['city']}, {snap['state']}", "median_price": snap["median_price"], "price_per_sqft": snap["price_per_sqft"], "median_days_on_market": snap["median_dom"], "price_change_yoy_pct": snap["price_change_yoy_pct"], "inventory_level": snap["inventory_level"], "walk_score": snap["walk_score"], "active_listings_count": snap["listings_count"], "estimated_median_monthly_rent": monthly_rent_estimate, "gross_rental_yield_pct": gross_yield, "market_summary": snap["market_summary"], "data_source": "MockProvider v1 — realistic 2024 US market estimates", }, } _cache_set(cache_key, result) _log_invocation("get_neighborhood_snapshot", location, (time.time() - _start) * 1000, True) return result async def search_listings( query: str, max_results: int = 5, min_beds: int | None = None, max_price: int | None = None, ) -> dict: """ Searches for listings matching a location query with optional filters. Args: query: City/neighborhood name (e.g. "Austin", "Seattle"). max_results: Cap on number of listings returned (default 5). min_beds: Minimum bedroom count filter (e.g. 3 → only 3+ bed listings). max_price: Maximum price filter in USD (e.g. 500000 → ≤$500k only). """ if not is_real_estate_enabled(): return _FEATURE_DISABLED_RESPONSE query = query.strip() tool_result_id = f"re_search_{query.lower().replace(' ', '_')}_{int(datetime.utcnow().timestamp())}" _start = time.time() # Cache key incorporates filters so filtered/unfiltered calls are stored separately cache_key = f"search:{query.lower()}:{max_results}:beds={min_beds}:price={max_price}" cached = _cache_get(cache_key) if cached: _log_invocation("search_listings", query, (time.time() - _start) * 1000, True) return cached city_key = _normalize_city(query) listings = list(_MOCK_LISTINGS.get(city_key, [])) if not listings: all_cities = list(_MOCK_LISTINGS.keys()) result = { "tool_name": "real_estate", "success": False, "tool_result_id": tool_result_id, "error": { "code": "REAL_ESTATE_PROVIDER_UNAVAILABLE", "message": ( f"No listings found for '{query}'. " f"Try one of: {', '.join(c.title() for c in all_cities)}." ), }, } _log_invocation("search_listings", query, (time.time() - _start) * 1000, False) return result # Apply optional filters before capping if min_beds is not None: listings = [l for l in listings if l["bedrooms"] >= min_beds] if max_price is not None: listings = [l for l in listings if l["price"] <= max_price] filters_applied = {} if min_beds is not None: filters_applied["min_beds"] = min_beds if max_price is not None: filters_applied["max_price"] = max_price capped = listings[:max_results] result = { "tool_name": "real_estate", "success": True, "tool_result_id": tool_result_id, "timestamp": datetime.utcnow().isoformat(), "result": { "query": query, "filters_applied": filters_applied, "total_returned": len(capped), "listings": capped, "data_source": "MockProvider v1 — realistic 2024 US market estimates", }, } _cache_set(cache_key, result) _log_invocation("search_listings", query, (time.time() - _start) * 1000, True) return result async def get_listing_details(listing_id: str) -> dict: """ Returns full detail for a single listing by its ID (e.g. 'atx-001'). """ if not is_real_estate_enabled(): return _FEATURE_DISABLED_RESPONSE listing_id = listing_id.strip().lower() tool_result_id = f"re_detail_{listing_id}_{int(datetime.utcnow().timestamp())}" _start = time.time() cache_key = f"detail:{listing_id}" cached = _cache_get(cache_key) if cached: _log_invocation("get_listing_details", listing_id, (time.time() - _start) * 1000, True) return cached for city_listings in _MOCK_LISTINGS.values(): for listing in city_listings: if listing["id"].lower() == listing_id: # Enrich with affordability metrics enriched = dict(listing) monthly_payment_est = round(listing["price"] * 0.8 * 0.00532, 0) # ~6.5% 30yr, 20% down annual_rent = listing["estimated_monthly_rent"] * 12 enriched["estimated_monthly_mortgage"] = monthly_payment_est enriched["annual_gross_rental_income"] = annual_rent enriched["gross_cap_rate_pct"] = listing["cap_rate_estimate"] result = { "tool_name": "real_estate", "success": True, "tool_result_id": tool_result_id, "timestamp": datetime.utcnow().isoformat(), "result": enriched, } _cache_set(cache_key, result) _log_invocation("get_listing_details", listing_id, (time.time() - _start) * 1000, True) return result result = { "tool_name": "real_estate", "success": False, "tool_result_id": tool_result_id, "error": { "code": "REAL_ESTATE_PROVIDER_UNAVAILABLE", "message": ( f"Listing '{listing_id}' not found. " "Use search_listings first to get valid listing IDs." ), }, } _log_invocation("get_listing_details", listing_id, (time.time() - _start) * 1000, False) return result async def compare_neighborhoods(location_a: str, location_b: str) -> dict: """ Compares two cities/neighborhoods side by side on key investment metrics. Returns a structured comparison useful for commute/affordability tradeoffs. """ if not is_real_estate_enabled(): return _FEATURE_DISABLED_RESPONSE tool_result_id = f"re_compare_{int(datetime.utcnow().timestamp())}" _start = time.time() snap_a = await get_neighborhood_snapshot(location_a) snap_b = await get_neighborhood_snapshot(location_b) failed = [] if not snap_a.get("success"): failed.append(location_a) if not snap_b.get("success"): failed.append(location_b) if failed: _log_invocation( "compare_neighborhoods", f"{location_a} vs {location_b}", (time.time() - _start) * 1000, False, ) return { "tool_name": "real_estate", "success": False, "tool_result_id": tool_result_id, "error": { "code": "REAL_ESTATE_PROVIDER_UNAVAILABLE", "message": f"Could not find data for: {', '.join(failed)}.", }, } a = snap_a["result"] b = snap_b["result"] def _winner(val_a, val_b, lower_is_better: bool = False): if lower_is_better: return a["location"] if val_a < val_b else b["location"] return a["location"] if val_a > val_b else b["location"] comparison = { "location_a": a["location"], "location_b": b["location"], "metrics": { "median_price": {"a": a["median_price"], "b": b["median_price"], "more_affordable": _winner(a["median_price"], b["median_price"], lower_is_better=True)}, "price_per_sqft": {"a": a["price_per_sqft"], "b": b["price_per_sqft"], "more_affordable": _winner(a["price_per_sqft"], b["price_per_sqft"], lower_is_better=True)}, "gross_rental_yield_pct": {"a": a["gross_rental_yield_pct"], "b": b["gross_rental_yield_pct"], "higher_yield": _winner(a["gross_rental_yield_pct"], b["gross_rental_yield_pct"])}, "days_on_market": {"a": a["median_days_on_market"], "b": b["median_days_on_market"], "less_competitive": _winner(a["median_days_on_market"], b["median_days_on_market"])}, "walk_score": {"a": a["walk_score"], "b": b["walk_score"], "more_walkable": _winner(a["walk_score"], b["walk_score"])}, "yoy_price_change_pct": {"a": a["price_change_yoy_pct"], "b": b["price_change_yoy_pct"]}, "inventory": {"a": a["inventory_level"], "b": b["inventory_level"]}, }, "summaries": { a["location"]: a["market_summary"], b["location"]: b["market_summary"], }, "data_source": "MockProvider v1 — realistic 2024 US market estimates", } result = { "tool_name": "real_estate", "success": True, "tool_result_id": tool_result_id, "timestamp": datetime.utcnow().isoformat(), "result": comparison, } _log_invocation( "compare_neighborhoods", f"{location_a} vs {location_b}", (time.time() - _start) * 1000, True, ) return result