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Implementation of phase 4.1

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This commit is contained in:
Nitzan Pomerantz
2025-10-26 10:58:46 +02:00
parent 4cb70c547b
commit 0ac9d136cd
13 changed files with 2115 additions and 373 deletions
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nadlan_mcp/fastmcp_server.py
+100 -105
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@@ -8,9 +8,10 @@ using the FastMCP library with simplified, working functions.
import json
import logging
from typing import List, Dict, Optional
from typing import List, Dict, Optional, Any
from mcp.server.fastmcp import FastMCP
from nadlan_mcp.govmap import GovmapClient
from nadlan_mcp.govmap.models import Deal, AutocompleteResponse
# Configure logging
logging.basicConfig(level=logging.INFO)
@@ -22,72 +23,72 @@ mcp = FastMCP("nadlan-mcp")
# Initialize the Govmap client
client = GovmapClient()
def strip_bloat_fields(deals: List[Dict]) -> List[Dict]:
def strip_bloat_fields(deals: List[Deal]) -> List[Dict[str, Any]]:
"""
Remove bloat fields from deal objects to reduce token usage in MCP responses.
Remove bloat fields from Deal models to reduce token usage in MCP responses.
Removes:
Converts Deal models to dictionaries and removes:
- shape: Large MULTIPOLYGON coordinate data (~40-50% of tokens, not useful for LLM analysis)
- sourceorder: Internal ordering field
- source_polygon_id: Internal reference field
- source_polygon_id: Internal reference field (only when it's a UUID string)
Args:
deals: List of deal dictionaries
deals: List of Deal model instances
Returns:
List of deals with bloat fields removed
List of deal dictionaries with bloat fields removed
"""
bloat_fields = {'shape', 'sourceorder', 'source_polygon_id'}
bloat_fields = {'shape', 'sourceorder'}
# Note: We keep source_polygon_id if it was added by our processing logic
return [
{k: v for k, v in deal.items() if k not in bloat_fields}
for deal in deals
]
result = []
for deal in deals:
# Convert Deal model to dict, excluding None values for cleaner output
deal_dict = deal.model_dump(exclude_none=True)
# Remove bloat fields
filtered_dict = {k: v for k, v in deal_dict.items() if k not in bloat_fields}
result.append(filtered_dict)
return result
@mcp.tool()
def autocomplete_address(search_text: str) -> str:
"""Search and autocomplete Israeli addresses.
Args:
search_text: The partial address to search for (in Hebrew or English)
Returns:
JSON string containing matching addresses with their coordinates
"""
try:
response = client.autocomplete_address(search_text)
if not response or 'results' not in response:
if not response.results:
return f"No addresses found for '{search_text}'"
# Format results for better readability
formatted_results = []
for result in response['results']:
# Parse coordinates from WKT POINT format: "POINT(longitude latitude)"
shape_str = result.get("shape", "")
coordinates = {}
if shape_str and shape_str.startswith("POINT("):
try:
coords_str = shape_str[6:-1] # Remove "POINT(" and ")"
coords = coords_str.split()
if len(coords) == 2:
coordinates = {
"longitude": float(coords[0]),
"latitude": float(coords[1])
}
except (ValueError, IndexError) as e:
logger.warning(f"Failed to parse coordinates from shape: {shape_str}, error: {e}")
for result in response.results:
result_dict = {
"text": result.text,
"id": result.id,
"type": result.type,
"score": result.score,
}
formatted_results.append({
"text": result.get("text", ""),
"id": result.get("id", ""),
"type": result.get("type", ""),
"score": result.get("score", 0),
"coordinates": coordinates
})
# Add coordinates if available
if result.coordinates:
result_dict["coordinates"] = {
"longitude": result.coordinates.longitude,
"latitude": result.coordinates.latitude
}
formatted_results.append(result_dict)
return json.dumps(formatted_results, ensure_ascii=False, indent=2)
except Exception as e:
logger.error(f"Error in autocomplete_address: {e}")
return f"Error searching for address: {str(e)}"
@@ -136,26 +137,19 @@ def get_street_deals(polygon_id: str, limit: int = 100, deal_type: int = 2) -> s
"""
try:
deals = client.get_street_deals(polygon_id, limit, deal_type=deal_type)
if not deals:
deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
return f"No {deal_type_desc} deals found for polygon ID {polygon_id}"
# Add price per sqm calculation for each deal
# Add deal type metadata
for deal in deals:
price = deal.get('dealAmount', 0)
area = deal.get('assetArea', 0)
if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0:
deal['price_per_sqm'] = round(price / area, 2)
else:
deal['price_per_sqm'] = None
# Add deal type info
deal['deal_type'] = deal_type
deal['deal_type_description'] = 'first_hand_new' if deal_type == 1 else 'second_hand_used'
# Calculate basic statistics including price per sqm
prices = [deal.get("dealAmount", 0) for deal in deals if deal.get("dealAmount")]
price_per_sqm_values = [deal.get("price_per_sqm", 0) for deal in deals if deal.get("price_per_sqm")]
deal.deal_type = deal_type
deal.deal_type_description = 'first_hand_new' if deal_type == 1 else 'second_hand_used'
# Calculate basic statistics using computed fields from models
prices = [deal.deal_amount for deal in deals if deal.deal_amount]
price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm]
stats = {}
if price_per_sqm_values:
@@ -196,20 +190,21 @@ def find_recent_deals_for_address(address: str, years_back: int = 2, radius_mete
"""
try:
deals = client.find_recent_deals_for_address(address, years_back, radius_meters, max_deals, deal_type)
if not deals:
deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
return f"No {deal_type_desc} deals found for address '{address}'"
# Calculate comprehensive statistics
prices = [deal.get("dealAmount", 0) for deal in deals if deal.get("dealAmount")]
areas = [deal.get("assetArea", 0) for deal in deals if deal.get("assetArea")]
price_per_sqm_values = [deal.get("price_per_sqm", 0) for deal in deals if deal.get("price_per_sqm")]
# Calculate comprehensive statistics using model attributes
prices = [deal.deal_amount for deal in deals if deal.deal_amount]
areas = [deal.asset_area for deal in deals if deal.asset_area]
price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm]
# Separate building, street and neighborhood deals for analysis
building_deals = [deal for deal in deals if deal.get("deal_source") == "same_building"]
street_deals = [deal for deal in deals if deal.get("deal_source") == "street"]
neighborhood_deals = [deal for deal in deals if deal.get("deal_source") == "neighborhood"]
# deal_source is added dynamically in find_recent_deals_for_address
building_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "same_building"]
street_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "street"]
neighborhood_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "neighborhood"]
stats = {
"deal_breakdown": {
@@ -280,26 +275,19 @@ def get_neighborhood_deals(polygon_id: str, limit: int = 100, deal_type: int = 2
"""
try:
deals = client.get_neighborhood_deals(polygon_id, limit, deal_type=deal_type)
if not deals:
deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
return f"No {deal_type_desc} deals found for polygon ID {polygon_id}"
# Add price per sqm calculation for each deal
# Add deal type metadata
for deal in deals:
price = deal.get('dealAmount', 0)
area = deal.get('assetArea', 0)
if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0:
deal['price_per_sqm'] = round(price / area, 2)
else:
deal['price_per_sqm'] = None
# Add deal type info
deal['deal_type'] = deal_type
deal['deal_type_description'] = 'first_hand_new' if deal_type == 1 else 'second_hand_used'
# Calculate basic statistics including price per sqm
prices = [deal.get("dealAmount", 0) for deal in deals if deal.get("dealAmount")]
price_per_sqm_values = [deal.get("price_per_sqm", 0) for deal in deals if deal.get("price_per_sqm")]
deal.deal_type = deal_type
deal.deal_type_description = 'first_hand_new' if deal_type == 1 else 'second_hand_used'
# Calculate basic statistics using computed fields from models
prices = [deal.deal_amount for deal in deals if deal.deal_amount]
price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm]
stats = {}
if price_per_sqm_values:
@@ -354,17 +342,17 @@ def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int
# Simplified processing - extract only essential data
for deal in deals:
date_str = deal.get('dealDate', '')
date_str = deal.deal_date
if not date_str:
continue
year = date_str[:4]
price = deal.get('dealAmount')
area = deal.get('assetArea')
price_per_sqm = deal.get('price_per_sqm')
prop_type = deal.get('assetTypeHeb', deal.get('propertyTypeDescription', 'לא ידוע'))
neighborhood = deal.get('settlementNameHeb', deal.get('neighborhood', 'לא ידוע'))
deal_source = deal.get('deal_source', 'unknown')
price = deal.deal_amount
area = deal.asset_area
price_per_sqm = deal.price_per_sqm
prop_type = deal.property_type_description or 'לא ידוע'
neighborhood = deal.settlement_name_heb or deal.neighborhood or 'לא ידוע'
deal_source = getattr(deal, 'deal_source', 'unknown')
if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0 and isinstance(price_per_sqm, (int, float)):
yearly_data[year].append({
@@ -488,12 +476,12 @@ def compare_addresses(addresses: List[str]) -> str:
deals = client.find_recent_deals_for_address(address, 2)
if deals:
prices = [deal.get("dealAmount", 0) for deal in deals if deal.get("dealAmount")]
areas = [deal.get("assetArea", 0) for deal in deals if deal.get("assetArea")]
price_per_sqm_values = [deal.get("price_per_sqm", 0) for deal in deals if deal.get("price_per_sqm")]
building_deals = [deal for deal in deals if deal.get("deal_source") == "same_building"]
street_deals = [deal for deal in deals if deal.get("deal_source") == "street"]
neighborhood_deals = [deal for deal in deals if deal.get("deal_source") == "neighborhood"]
prices = [deal.deal_amount for deal in deals if deal.deal_amount]
areas = [deal.asset_area for deal in deals if deal.asset_area]
price_per_sqm_values = [deal.price_per_sqm for deal in deals if deal.price_per_sqm]
building_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "same_building"]
street_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "street"]
neighborhood_deals = [deal for deal in deals if getattr(deal, "deal_source", None) == "neighborhood"]
comparison = {
"address": address,
@@ -644,7 +632,7 @@ def get_valuation_comparables(
# Calculate statistics on filtered comparables
stats = client.calculate_deal_statistics(filtered_deals)
return json.dumps({
"address": address,
"years_back": years_back,
@@ -656,7 +644,7 @@ def get_valuation_comparables(
"floor": f"{min_floor}-{max_floor}" if min_floor or max_floor else None,
},
"total_comparables": len(filtered_deals),
"statistics": stats,
"statistics": stats.model_dump(exclude_none=True), # Serialize DealStatistics model
"comparables": strip_bloat_fields(filtered_deals)
}, ensure_ascii=False, indent=2)
@@ -712,7 +700,7 @@ def get_deal_statistics(
# Calculate statistics
stats = client.calculate_deal_statistics(deals)
return json.dumps({
"address": address,
"years_back": years_back,
@@ -720,7 +708,7 @@ def get_deal_statistics(
"property_type": property_type,
"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
},
"statistics": stats
"statistics": stats.model_dump(exclude_none=True) # Serialize DealStatistics model
}, ensure_ascii=False, indent=2)
except Exception as e:
@@ -737,13 +725,18 @@ def _safe_calculate_metric(metric_func, deals):
Args:
metric_func: Function to call with deals as argument
deals: List of deal dictionaries to analyze
deals: List of Deal model instances to analyze
Returns:
Result dictionary from metric_func, or error dictionary if ValueError raised
Result dictionary from metric_func (serialized from Pydantic model),
or error dictionary if ValueError raised
"""
try:
return metric_func(deals)
result = metric_func(deals)
# Serialize Pydantic model to dict
if hasattr(result, 'model_dump'):
return result.model_dump(exclude_none=True)
return result
except ValueError as e:
return {"error": str(e)}
@@ -799,8 +792,10 @@ def get_market_activity_metrics(
"market_liquidity": liquidity_metrics,
"investment_potential": investment_metrics,
"summary": {
"activity_level": activity_metrics.get("activity_level"),
"liquidity_rating": liquidity_metrics.get("liquidity_rating"),
"activity_score": activity_metrics.get("activity_score"),
"activity_trend": activity_metrics.get("trend"),
"liquidity_score": liquidity_metrics.get("liquidity_score"),
"market_activity_level": liquidity_metrics.get("market_activity_level"),
"investment_score": investment_metrics.get("investment_score"),
"price_trend": investment_metrics.get("price_trend"),
"market_stability": investment_metrics.get("market_stability")