Add: Return outlier deals in analysis responses
Feature: - New parameter `include_outlier_deals` (default=True) in: - filter_deals_for_analysis() - calculate_deal_statistics() - get_valuation_comparables() MCP tool - get_deal_statistics() MCP tool Behavior: - When ON: response includes `outlier_deals` field with removed deals - LLM can see what was filtered out, answer questions about it - Maintains full transparency on outlier removal Terminology fixed: - "outlier_deals" = deals removed as outliers (clearer than "filtered_deals") - "filtered deals" = deals that PASSED filtering - Added to OutlierReport model All 311 tests pass 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Nitzan Pomerantz
@@ -799,6 +799,7 @@ def get_valuation_comparables(
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radius_meters: int = 100,
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max_comparables: int = 50,
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iqr_multiplier: Optional[float] = None,
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include_outlier_deals: bool = True,
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) -> str:
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"""Get comparable properties for valuation analysis.
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@@ -822,16 +823,19 @@ def get_valuation_comparables(
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radius_meters: Search radius in meters (default: 100, larger than find_recent_deals to get more comparables)
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max_comparables: Maximum number of deals to return (default: 50, optimized for MCP token limits)
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iqr_multiplier: Override IQR multiplier for outlier detection (default: 1.0). Lower = more aggressive filtering
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include_outlier_deals: If True (default), include the outlier deals separately in the response
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This allows you to see what was filtered out and answer questions about it
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Returns:
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JSON string containing:
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- Filtered comparable deals with full details
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- deals: Comparable deals that passed filtering
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- deal_breakdown with outlier filtering metadata:
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- total_deals: Count after filtering
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- total_deals_before_filtering: Count before filtering
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- outliers_removed: Number of deals filtered out
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- outliers_removed: Number of deals removed as outliers
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- filtering_method: Method used (e.g., "iqr")
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- iqr_multiplier: IQR multiplier used (e.g., 1.0)
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- outlier_deals: List of deals that were removed as outliers (if include_outlier_deals=True)
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"""
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log_mcp_call(
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"get_valuation_comparables",
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@@ -905,7 +909,11 @@ def get_valuation_comparables(
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):
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deals_before_outlier_filter = len(filtered_deals)
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filtered_deals, outlier_report = filter_deals_for_analysis(
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filtered_deals, config, metric="price_per_sqm", iqr_multiplier=iqr_multiplier
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filtered_deals,
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config,
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metric="price_per_sqm",
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iqr_multiplier=iqr_multiplier,
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include_outlier_deals=include_outlier_deals,
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)
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effective_k = (
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iqr_multiplier if iqr_multiplier is not None else config.analysis_iqr_multiplier
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@@ -936,35 +944,37 @@ def get_valuation_comparables(
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if outlier_report["method_used"] == "iqr":
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deal_breakdown["iqr_multiplier"] = outlier_report["parameters"]["iqr_multiplier"]
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# Normalize response structure to match other tools
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return json.dumps(
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{
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"search_parameters": {
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"address": address,
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"years_back": years_back,
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"radius_meters": radius_meters,
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"max_comparables": max_comparables,
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"filters_applied": {
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"property_type": property_type,
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"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
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"price": f"{min_price}-{max_price}" if min_price or max_price else None,
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"area": f"{min_area}-{max_area}" if min_area or max_area else None,
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"floor": f"{min_floor}-{max_floor}" if min_floor or max_floor else None,
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},
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# Build response with filtered deals
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response_data = {
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"search_parameters": {
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"address": address,
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"years_back": years_back,
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"radius_meters": radius_meters,
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"max_comparables": max_comparables,
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"filters_applied": {
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"property_type": property_type,
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"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
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"price": f"{min_price}-{max_price}" if min_price or max_price else None,
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"area": f"{min_area}-{max_area}" if min_area or max_area else None,
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"floor": f"{min_floor}-{max_floor}" if min_floor or max_floor else None,
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},
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"market_statistics": {
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"deal_breakdown": deal_breakdown,
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"price_statistics": stats.price_statistics,
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"area_statistics": stats.area_statistics,
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"price_per_sqm_statistics": stats.price_per_sqm_statistics,
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"property_type_distribution": stats.property_type_distribution,
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"date_range": stats.date_range,
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},
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"deals": strip_bloat_fields(filtered_deals),
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},
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ensure_ascii=False,
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indent=2,
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)
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"market_statistics": {
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"deal_breakdown": deal_breakdown,
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"price_statistics": stats.price_statistics,
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"area_statistics": stats.area_statistics,
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"price_per_sqm_statistics": stats.price_per_sqm_statistics,
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"property_type_distribution": stats.property_type_distribution,
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"date_range": stats.date_range,
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},
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"deals": strip_bloat_fields(filtered_deals),
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}
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# Add outlier deals if present and requested
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if include_outlier_deals and outlier_report and "outlier_deals" in outlier_report:
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response_data["outlier_deals"] = strip_bloat_fields(outlier_report["outlier_deals"])
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return json.dumps(response_data, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Error in get_valuation_comparables: {e}", exc_info=True)
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@@ -979,6 +989,7 @@ def get_deal_statistics(
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min_rooms: Optional[float] = None,
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max_rooms: Optional[float] = None,
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iqr_multiplier: Optional[float] = None,
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include_outlier_deals: bool = True,
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) -> str:
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"""Calculate statistical aggregations on deal data for an address.
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@@ -992,9 +1003,13 @@ def get_deal_statistics(
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min_rooms: Minimum number of rooms
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max_rooms: Maximum number of rooms
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iqr_multiplier: Override IQR multiplier for outlier detection (default: 1.0). Lower = more aggressive filtering
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include_outlier_deals: If True (default), include the outlier deals in the response
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This allows you to see what was filtered out
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Returns:
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JSON string containing statistical metrics (mean, median, percentiles, etc.)
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JSON string containing:
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- market_statistics: Statistical metrics (mean, median, percentiles, etc.)
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- outlier_deals: Deals that were removed as outliers (if include_outlier_deals=True)
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"""
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log_mcp_call(
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"get_deal_statistics",
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@@ -1033,35 +1048,39 @@ def get_deal_statistics(
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)
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# Calculate statistics
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stats = client.calculate_deal_statistics(deals, iqr_multiplier=iqr_multiplier)
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# Normalize response structure to match other tools
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return json.dumps(
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{
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"search_parameters": {
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"address": address,
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"years_back": years_back,
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"filters_applied": {
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"property_type": property_type,
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"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
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},
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},
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"market_statistics": {
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"deal_breakdown": {
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"total_deals": stats.total_deals,
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},
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"price_statistics": stats.price_statistics,
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"area_statistics": stats.area_statistics,
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"price_per_sqm_statistics": stats.price_per_sqm_statistics,
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"property_type_distribution": stats.property_type_distribution,
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"date_range": stats.date_range,
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},
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"deals": [], # Statistics-only query, no full deals returned
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},
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ensure_ascii=False,
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indent=2,
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stats = client.calculate_deal_statistics(
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deals, iqr_multiplier=iqr_multiplier, include_outlier_deals=include_outlier_deals
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)
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# Build response
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response_data = {
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"search_parameters": {
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"address": address,
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"years_back": years_back,
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"filters_applied": {
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"property_type": property_type,
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"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
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},
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},
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"market_statistics": {
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"deal_breakdown": {
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"total_deals": stats.total_deals,
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},
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"price_statistics": stats.price_statistics,
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"area_statistics": stats.area_statistics,
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"price_per_sqm_statistics": stats.price_per_sqm_statistics,
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"property_type_distribution": stats.property_type_distribution,
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"date_range": stats.date_range,
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},
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"deals": [], # Statistics-only query, no full deals returned
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}
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# Add outlier deals if present and requested
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if include_outlier_deals and stats.outlier_report and stats.outlier_report.outlier_deals:
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response_data["outlier_deals"] = strip_bloat_fields(stats.outlier_report.outlier_deals)
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return json.dumps(response_data, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.error(f"Error in get_deal_statistics: {e}", exc_info=True)
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return f"Error calculating deal statistics: {str(e)}"
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