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Normalize MCP response structures across all tools

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Implement changes from MCP_NORMALIZATION_FIX.md to provide consistent
response structure across all MCP tools. This fixes the bot integration
issue where different tools returned data in different structures.

Changes:
- get_valuation_comparables:
  - Rename "comparables" → "deals"
  - Move "total_comparables" → "market_statistics.deal_breakdown.total_deals"
  - Add "search_parameters" section with "filters_applied"
  - Move "statistics" → "market_statistics"

- find_recent_deals_for_address:
  - Rename "price_stats.average_price" → "price_statistics.mean"
  - Rename "price_stats.median_price" → "price_statistics.median"
  - Rename "area_stats" → "area_statistics"
  - Rename "price_per_sqm_stats" → "price_per_sqm_statistics"

- get_deal_statistics:
  - Add "search_parameters" section
  - Move "statistics" → "market_statistics"
  - Add "market_statistics.deal_breakdown.total_deals"

- analyze_market_trends:
  - Add "market_statistics.deal_breakdown.total_deals"
  - Keep existing tool-specific fields (yearly_trends, etc.)

- get_market_activity_metrics:
  - Add "market_statistics.deal_breakdown.total_deals"
  - Keep existing tool-specific metrics

All tools now follow standard structure:
{
  "search_parameters" or "analysis_parameters": {...},
  "market_statistics": {
    "deal_breakdown": {"total_deals": N},
    "price_statistics": {"mean": ..., "median": ...},
    "area_statistics": {...},
    "price_per_sqm_statistics": {...}
  },
  "deals": [...]
}

Updated tests to match new normalized structure.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Nitzan P
2025-11-23 09:49:52 +02:00
parent c444e88a10
commit e791d3c1ed
4 changed files with 476 additions and 76 deletions
Download Patch File Download Diff File Expand all files Collapse all files
nadlan_mcp/fastmcp_server.py
+111 -52
View File
@@ -258,33 +258,30 @@ def find_recent_deals_for_address(
}
}
# Standardize field names to match other tools
if prices:
stats["price_stats"] = {
"average_price": round(sum(prices) / len(prices), 0),
"min_price": min(prices),
"max_price": max(prices),
"median_price": sorted(prices)[len(prices) // 2] if prices else 0,
"total_volume": sum(prices),
stats["price_statistics"] = {
"mean": round(sum(prices) / len(prices), 0),
"min": min(prices),
"max": max(prices),
"median": sorted(prices)[len(prices) // 2] if prices else 0,
"total": sum(prices),
}
if areas:
stats["area_stats"] = {
"average_area": round(sum(areas) / len(areas), 1),
"min_area": min(areas),
"max_area": max(areas),
"median_area": sorted(areas)[len(areas) // 2] if areas else 0,
stats["area_statistics"] = {
"mean": round(sum(areas) / len(areas), 1),
"min": min(areas),
"max": max(areas),
"median": sorted(areas)[len(areas) // 2] if areas else 0,
}
if price_per_sqm_values:
stats["price_per_sqm_stats"] = {
"average_price_per_sqm": round(
sum(price_per_sqm_values) / len(price_per_sqm_values), 0
),
"min_price_per_sqm": round(min(price_per_sqm_values), 0),
"max_price_per_sqm": round(max(price_per_sqm_values), 0),
"median_price_per_sqm": round(
sorted(price_per_sqm_values)[len(price_per_sqm_values) // 2], 0
)
stats["price_per_sqm_statistics"] = {
"mean": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 0),
"min": round(min(price_per_sqm_values), 0),
"max": round(max(price_per_sqm_values), 0),
"median": round(sorted(price_per_sqm_values)[len(price_per_sqm_values) // 2], 0)
if price_per_sqm_values
else 0,
}
@@ -531,6 +528,7 @@ def analyze_market_trends(
deal_type_desc = "first hand (new)" if deal_type == 1 else "second hand (used)"
# Return summarized analysis (NO raw deals to save tokens)
# Normalize structure with standard market_statistics while keeping tool-specific analysis
return json.dumps(
{
"analysis_parameters": {
@@ -541,8 +539,12 @@ def analyze_market_trends(
"deal_type": deal_type,
"deal_type_description": deal_type_desc,
},
"market_statistics": {
"deal_breakdown": {
"total_deals": len(deals),
},
},
"market_summary": {
"total_deals": len(deals),
"years_with_data": len(yearly_trends),
"unique_property_types": len(property_type_analysis),
"unique_neighborhoods": len(neighborhood_analysis),
@@ -564,6 +566,7 @@ def analyze_market_trends(
else None,
"deal_source_summary": f"Building: {len([d for d in deals if getattr(d, 'deal_source', None) == 'same_building'])}, Street: {len([d for d in deals if getattr(d, 'deal_source', None) == 'street'])}, Neighborhood: {len([d for d in deals if getattr(d, 'deal_source', None) == 'neighborhood'])}",
},
"deals": [], # Trend analysis doesn't return raw deals to save tokens
},
ensure_ascii=False,
indent=2,
@@ -756,9 +759,18 @@ def get_valuation_comparables(
if not deals:
return json.dumps(
{
"address": address,
"years_back": years_back,
"comparables": [],
"search_parameters": {
"address": address,
"years_back": years_back,
"radius_meters": radius_meters,
"max_comparables": max_comparables,
},
"market_statistics": {
"deal_breakdown": {
"total_deals": 0,
},
},
"deals": [],
"message": "No deals found for this address",
},
ensure_ascii=False,
@@ -782,20 +794,33 @@ def get_valuation_comparables(
# Calculate statistics on filtered comparables
stats = client.calculate_deal_statistics(filtered_deals)
# Normalize response structure to match other tools
return json.dumps(
{
"address": address,
"years_back": years_back,
"filters_applied": {
"property_type": property_type,
"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
"price": f"{min_price}-{max_price}" if min_price or max_price else None,
"area": f"{min_area}-{max_area}" if min_area or max_area else None,
"floor": f"{min_floor}-{max_floor}" if min_floor or max_floor else None,
"search_parameters": {
"address": address,
"years_back": years_back,
"radius_meters": radius_meters,
"max_comparables": max_comparables,
"filters_applied": {
"property_type": property_type,
"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
"price": f"{min_price}-{max_price}" if min_price or max_price else None,
"area": f"{min_area}-{max_area}" if min_area or max_area else None,
"floor": f"{min_floor}-{max_floor}" if min_floor or max_floor else None,
},
},
"total_comparables": len(filtered_deals),
"statistics": stats.model_dump(exclude_none=True), # Serialize DealStatistics model
"comparables": strip_bloat_fields(filtered_deals),
"market_statistics": {
"deal_breakdown": {
"total_deals": len(filtered_deals),
},
"price_statistics": stats.price_statistics,
"area_statistics": stats.area_statistics,
"price_per_sqm_statistics": stats.price_per_sqm_statistics,
"property_type_distribution": stats.property_type_distribution,
"date_range": stats.date_range,
},
"deals": strip_bloat_fields(filtered_deals),
},
ensure_ascii=False,
indent=2,
@@ -836,9 +861,15 @@ def get_deal_statistics(
if not deals:
return json.dumps(
{
"address": address,
"years_back": years_back,
"statistics": {"count": 0, "message": "No deals found for this address"},
"search_parameters": {
"address": address,
"years_back": years_back,
},
"market_statistics": {
"deal_breakdown": {"total_deals": 0},
"message": "No deals found for this address",
},
"deals": [],
},
ensure_ascii=False,
indent=2,
@@ -853,15 +884,28 @@ def get_deal_statistics(
# Calculate statistics
stats = client.calculate_deal_statistics(deals)
# Normalize response structure to match other tools
return json.dumps(
{
"address": address,
"years_back": years_back,
"filters_applied": {
"property_type": property_type,
"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
"search_parameters": {
"address": address,
"years_back": years_back,
"filters_applied": {
"property_type": property_type,
"rooms": f"{min_rooms}-{max_rooms}" if min_rooms or max_rooms else None,
},
},
"statistics": stats.model_dump(exclude_none=True), # Serialize DealStatistics model
"market_statistics": {
"deal_breakdown": {
"total_deals": stats.total_deals,
},
"price_statistics": stats.price_statistics,
"area_statistics": stats.area_statistics,
"price_per_sqm_statistics": stats.price_per_sqm_statistics,
"property_type_distribution": stats.property_type_distribution,
"date_range": stats.date_range,
},
"deals": [], # Statistics-only query, no full deals returned
},
ensure_ascii=False,
indent=2,
@@ -924,10 +968,18 @@ def get_market_activity_metrics(address: str, years_back: int = 2, radius_meters
if not deals:
return json.dumps(
{
"address": address,
"analysis_parameters": {
"address": address,
"years_back": years_back,
"radius_meters": radius_meters,
},
"market_statistics": {
"deal_breakdown": {
"total_deals": 0,
},
},
"deals": [],
"error": "No deals found for analysis",
"years_back": years_back,
"radius_meters": radius_meters,
},
ensure_ascii=False,
indent=2,
@@ -938,13 +990,19 @@ def get_market_activity_metrics(address: str, years_back: int = 2, radius_meters
liquidity_metrics = _safe_calculate_metric(client.get_market_liquidity, deals)
investment_metrics = _safe_calculate_metric(client.analyze_investment_potential, deals)
# Combine all metrics
# Combine all metrics with normalized structure
return json.dumps(
{
"address": address,
"years_back": years_back,
"radius_meters": radius_meters,
"total_deals_analyzed": len(deals),
"analysis_parameters": {
"address": address,
"years_back": years_back,
"radius_meters": radius_meters,
},
"market_statistics": {
"deal_breakdown": {
"total_deals": len(deals),
},
},
"market_activity": activity_metrics,
"market_liquidity": liquidity_metrics,
"investment_potential": investment_metrics,
@@ -957,6 +1015,7 @@ def get_market_activity_metrics(address: str, years_back: int = 2, radius_meters
"price_trend": investment_metrics.get("price_trend"),
"market_stability": investment_metrics.get("market_stability"),
},
"deals": [], # Activity metrics don't return raw deals
},
ensure_ascii=False,
indent=2,