Refactor and enhance various components of the FINN real estate analysis tool

- Updated docker-compose files to use local data volumes for development.
- Refactored analysis.py to improve code readability and performance, including changes to cache age calculations and hash computations.
- Enhanced cache.py to ensure the database directory is created if it doesn't exist and improved SQL query formatting.
- Modified cli.py to improve logging and statistics reporting for finn_ads.
- Updated config.py to streamline environment variable handling.
- Initialized the database eagerly in http_server.py to prevent runtime errors.
- Refactored mcp_server.py to slim down data structures and improve response formatting for API calls.
- Enhanced service.py to improve feedback handling and shortlist retrieval, ensuring enriched data is returned.
- Updated recompute_analysis_cache.py for better SQL query formatting.

finn_eiendom/analysis.py

@@ -132,9 +132,7 @@ def _compute_deps_hash(
     """
     ad_hash = get_finn_ad_hash(conn, finnkode)
     unit_hash = get_eiendom_unit_hash(conn, unit_code) if unit_code else None
-    comps_hash = (
-        get_similar_units_hash(conn, unit_code, listing_status) if unit_code else None
-    )
+    comps_hash = get_similar_units_hash(conn, unit_code, listing_status) if unit_code else None
     return combine_hashes(ad_hash, unit_hash, comps_hash)
 
 
@@ -225,7 +223,7 @@ async def analyze_ad(
     from datetime import datetime, UTC, timedelta
 
     price_history = get_price_history(conn, finn_ad.finnkode, limit=20)
-    
+
     # Compute cache age: how long since we last fetched this ad
     cursor = conn.cursor()
     cursor.execute(
@@ -239,11 +237,11 @@ async def analyze_ad(
         last_verified = db_row["last_verified_at"]
         if last_verified:
             last_verified_at = datetime.fromisoformat(last_verified)
-            structural_age_days = (datetime.now(UTC) - fetched_at).days
-            price_age_hours = (datetime.now(UTC) - last_verified_at).total_seconds() / 3600
+            structural_age_mins = (datetime.now(UTC) - fetched_at).total_seconds() / 60
+            price_age_mins = (datetime.now(UTC) - last_verified_at).total_seconds() / 60
             cache_age = {
-                "structural_days": structural_age_days,
-                "price_hours": round(price_age_hours, 1),
+                "structural_minutes": round(structural_age_mins, 1),
+                "price_minutes": round(price_age_mins, 1),
             }
 
     result = {
@@ -282,6 +280,7 @@ async def analyze_ad(
     # Round-trip through JSON to guarantee all values are serialisable
     # (catches any datetime that survives model_dump, e.g. from scoring).
     import json as _json
+
     result = _json.loads(_json.dumps(result, default=str))
 
     save_analysis(conn, finn_ad.finnkode, deps_hash, result)
@@ -301,7 +300,9 @@ async def _fetch_card_to_db(
     treats None as a skip without aborting the whole batch.
     """
     try:
-        finn_ad = cache.get_finn_ad(conn, card.finnkode, ttl_hours=FINN_CACHE_TTL_AD_STRUCTURAL_DAYS * 24)
+        finn_ad = cache.get_finn_ad(
+            conn, card.finnkode, ttl_hours=FINN_CACHE_TTL_AD_STRUCTURAL_DAYS * 24
+        )
         if finn_ad is None:
             finn_ad = await ad_module.fetch_ad_details(card.finnkode, client=client)
             save_finn_ad(conn, finn_ad)
@@ -364,9 +365,7 @@ async def analyze_search(
     skipped_count = len(cards[:detail_limit]) - len(resale_cards)
 
     if ctx is not None:
-        await ctx.info(
-            f"Found {len(cards)} listings, {len(resale_cards)} resale ads to fetch."
-        )
+        await ctx.info(f"Found {len(cards)} listings, {len(resale_cards)} resale ads to fetch.")
 
     # ------------------------------------------------------------------
     # Phase 1: parallel fetch to DB
@@ -424,8 +423,7 @@ async def analyze_search(
 
     if ctx is not None:
         await ctx.info(
-            f"Done. {len(results)} analyzed, {enriched_count} enriched, "
-            f"{skipped_count} skipped."
+            f"Done. {len(results)} analyzed, {enriched_count} enriched, {skipped_count} skipped."
         )
 
     # Record this search run in the database
@@ -442,4 +440,4 @@ async def analyze_search(
             "skipped_listings": skipped_count,
             "eiendom_enriched": enriched_count,
         },
-    }
+    }