Enhance analysis functionality with parallel fetching and response shaping; add image processing for unit images

finn_eiendom/analysis.py

@@ -16,7 +16,9 @@ The cached result is invalidated automatically the moment any piece of
 underlying data changes, because the deps_hash will differ.
 """
 
+import asyncio
 import logging
+from typing import Any
 
 from . import ad as ad_module
 from . import cache, eiendom_no, scoring, search
@@ -43,6 +45,10 @@ from .models import EiendomUnit, FinnAd, SimilarUnit
 
 logger = logging.getLogger(__name__)
 
+# Max parallel ad + eiendom.no fetches in analyze_search phase 1.
+# High enough to be fast; low enough to avoid FINN rate-limiting.
+FETCH_CONCURRENCY = 5
+
 
 def _normalize_description(text: str | None) -> str:
     return text.lower() if text else ""
@@ -91,8 +97,6 @@ def _build_ad_summary(
         risks.append("Risk flags are detected in description or metadata.")
     if ad.common_costs and ad.common_costs > 5000:
         risks.append("Common costs are relatively high and should be reviewed.")
-    if enriched and enriched.sale_status and enriched.sale_status.upper() != "FOR_SALE":
-        risks.append("Eiendom.no sale status does not indicate an active sale.")
     if not enriched:
         risks.append("Missing Eiendom.no data increases uncertainty.")
 
@@ -208,8 +212,27 @@ async def analyze_ad(
 
     result = {
         "finnkode": finn_ad.finnkode,
+        "url": finn_ad.url,
         "title": finn_ad.title,
         "address": finn_ad.address,
+        "district": finn_ad.district,
+        "property_type": finn_ad.property_type,
+        "ownership_type": finn_ad.ownership_type,
+        "floor": finn_ad.floor,
+        "area_m2": finn_ad.area_m2,
+        "bedrooms": finn_ad.bedrooms,
+        "rooms": finn_ad.rooms,
+        "total_price": finn_ad.total_price,
+        "asking_price": finn_ad.asking_price,
+        "shared_debt": finn_ad.shared_debt,
+        "common_costs": finn_ad.common_costs,
+        "construction_year": finn_ad.construction_year,
+        "has_balcony": finn_ad.has_balcony,
+        "has_terrace": finn_ad.has_terrace,
+        "has_elevator": finn_ad.has_elevator,
+        "has_parking": finn_ad.has_parking,
+        "has_garage": finn_ad.has_garage,
+        "eiendom_unit_code": finn_ad.eiendom_unit_code,
         "score": scores,
         "categories": categories,
         "summary": summary,
@@ -226,12 +249,26 @@ async def analyze_ad(
     return result
 
 
-async def _analyze_card(card, conn, *, include_eiendom_no: bool, client) -> dict:
+async def _fetch_card_to_db(
-    """Fetch details + enrich a single search card. Raises on unrecoverable
+    card,
-    errors; the caller is responsible for catching and skipping."""
+    conn,
+    *,
+    include_eiendom_no: bool,
+    client,
+) -> tuple["FinnAd | None", "str | None"]:
+    """Phase 1 worker: fetch ad details + resolve Eiendom.no unit, persist to DB.
+
+    Returns (finn_ad, unit_code).  Both can be None on failure -- the caller
+    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_HOURS)
         if finn_ad is None:
             finn_ad = await ad_module.fetch_ad_details(card.finnkode, client=client)
+            save_finn_ad(conn, finn_ad)
+    except Exception as exc:
+        logger.warning("Failed to fetch ad %s: %s", card.finnkode, exc)
+        return None, None
 
     unit_code = None
     if include_eiendom_no:
@@ -239,11 +276,9 @@ async def _analyze_card(card, conn, *, include_eiendom_no: bool, client) -> dict
             matched_unit = await eiendom_no.search_unit_from_finn_url(card.url)
             unit_code = matched_unit.unit_code if matched_unit else None
         except Exception as exc:
-            # A failed unit resolution is non-fatal -- proceed without enrichment.
             logger.warning("Eiendom.no unit search failed for %s: %s", card.finnkode, exc)
-            unit_code = None
 
-    return await analyze_ad(finn_ad, unit_code=unit_code)
+    return finn_ad, unit_code
 
 
 async def analyze_search(
@@ -254,13 +289,24 @@ async def analyze_search(
     include_eiendom_no: bool = True,
     client=None,
     use_cache: bool = True,
+    ctx: Any = None,
 ) -> dict:
     """Analyze a FINN search URL and enrich matching listings.
 
-    Search-level results are NOT cached as a whole (the search page itself
+    Two-phase parallel execution
-    is cached at the HTML level).  Individual ad analyses ARE cached via
+    ----------------------------
-    ``analyze_ad``, so re-running a search only re-scores ads whose
+    Phase 1 (parallel, I/O bound):
-    underlying data has changed.
+      All resale cards are fetched concurrently behind a semaphore of size
+      ``FETCH_CONCURRENCY``.  Each worker fetches the ad detail page and
+      resolves the Eiendom.no unit in one shot, then writes both to SQLite.
+      Progress is reported via ``ctx`` if provided.
+
+    Phase 2 (sequential, cache bound):
+      Scoring reads entirely from SQLite -- no network -- and is fast.
+      Results are sorted by total score and returned.
+
+    Individual ad analyses ARE cached via ``analyze_ad``; re-running a search
+    only re-scores ads whose underlying data has changed.
     """
     conn = cache.init_db(FINN_CACHE_PATH)
     cards = await search.fetch_search_pages(
@@ -269,38 +315,75 @@ async def analyze_search(
         client=client,
         use_cache=use_cache,
     )
-    results = []
-    enriched_count = 0
-    skipped_count = 0
-    cache_hits = 0
 
-    if fetch_details:
+    resale_cards = [c for c in cards[:detail_limit] if _is_resale_listing(c.url)]
-        for card in cards[:detail_limit]:
+    skipped_count = len(cards[:detail_limit]) - len(resale_cards)
-            # Project / new-build ads are not resale listings and fetch_ad_details
-            # cannot resolve them -- skip up front rather than 404 mid-run.
-            if not _is_resale_listing(card.url):
-                logger.info("Skipping non-resale card %s (%s)", card.finnkode, card.url)
-                skipped_count += 1
-                continue
 
-            # One bad card (stale finnkode, removed ad, transient network error)
+    if ctx is not None:
-            # must not abort the whole search -- isolate each card.
+        await ctx.info(
-            try:
+            f"Found {len(cards)} listings, {len(resale_cards)} resale ads to fetch."
-                result = await _analyze_card(
+        )
+
+    # ------------------------------------------------------------------
+    # Phase 1: parallel fetch to DB
+    # ------------------------------------------------------------------
+    fetched: dict[str, tuple] = {}  # finnkode -> (FinnAd, unit_code | None)
+    fetch_counter = 0
+    sem = asyncio.Semaphore(FETCH_CONCURRENCY)
+
+    if not fetch_details:
+        resale_cards = []
+
+    async def _fetch_worker(card, idx: int) -> None:
+        nonlocal fetch_counter
+        async with sem:
+            finn_ad, unit_code = await _fetch_card_to_db(
                 card, conn, include_eiendom_no=include_eiendom_no, client=client
             )
+            fetched[card.finnkode] = (finn_ad, unit_code)
+            fetch_counter += 1
+            if ctx is not None:
+                await ctx.report_progress(fetch_counter, len(resale_cards))
+                status = "enriched" if unit_code else "no eiendom match"
+                await ctx.info(
+                    f"[{fetch_counter}/{len(resale_cards)}] {card.finnkode} fetched ({status})"
+                )
+
+    await asyncio.gather(*[_fetch_worker(c, i) for i, c in enumerate(resale_cards)])
+
+    # ------------------------------------------------------------------
+    # Phase 2: score from DB (reads cache, fast)
+    # ------------------------------------------------------------------
+    if ctx is not None:
+        await ctx.info(f"All data fetched. Scoring {len(resale_cards)} ads...")
+
+    results = []
+    enriched_count = 0
+
+    for card in resale_cards:
+        finn_ad, unit_code = fetched.get(card.finnkode, (None, None))
+        if finn_ad is None:
+            skipped_count += 1
+            continue
+        try:
+            result = await analyze_ad(finn_ad, unit_code=unit_code)
         except Exception as exc:
-                logger.warning("Skipping card %s: %s", card.finnkode, exc)
+            logger.warning("Skipping card %s during scoring: %s", card.finnkode, exc)
             skipped_count += 1
             continue
 
         if result.get("eiendom_unit"):
             enriched_count += 1
-            # Track analysis cache hits via the absence of recompute logging
-            # (the flag is not propagated up here; rely on debug logs).
         results.append(result)
 
     results.sort(key=lambda item: item["score"].get("total", 0.0), reverse=True)
+
+    if ctx is not None:
+        await ctx.info(
+            f"Done. {len(results)} analyzed, {enriched_count} enriched, "
+            f"{skipped_count} skipped."
+        )
+
     return {
         "search_url": search_url,
         "search_cards": [card.model_dump(mode="json") for card in cards],

finn_eiendom/mcp_server.py

@@ -1,11 +1,15 @@
 """FastMCP stdio server for FINN real estate analysis and Eiendom.no enrichment."""
 
+import base64
 import json
 import logging
 from typing import Any
 import os
+import asyncio
+import httpx
 from mcp.server.transport_security import TransportSecuritySettings
-from mcp.server.fastmcp import FastMCP
+from mcp.server.fastmcp import Context, FastMCP
+from mcp.types import ImageContent, TextContent
 
 from .eiendom_no import (
     build_unit_vector,
@@ -39,6 +43,120 @@ from .service import (
 logger = logging.getLogger(__name__)
 
 
+# ---------------------------------------------------------------------------
+# Response shaping
+# ---------------------------------------------------------------------------
+
+
+def _slim_listing(rank: int, item: dict) -> dict:
+    """Collapse one full analyze_ad result into a compact listing card.
+
+    Drops: listing_description, unit_images, unit_vector, all timestamps,
+    full similar_units list, score dimension breakdown.
+    Derives: avg_comp_sqm_price from similar_units.
+    """
+    eu = item.get("eiendom_unit") or {}
+    comps = item.get("similar_units") or []
+    sqm_prices = [c["sqm_price"] for c in comps if c.get("sqm_price")]
+    avg_comp_sqm = round(sum(sqm_prices) / len(sqm_prices)) if sqm_prices else None
+
+    # Slim comps: drop internal IDs, coords, redundant status fields.
+    # Sort by recency, keep 15 most recent — older comps lose relevance fast.
+    def _slim_comp(c: dict) -> dict:
+        return {
+            "unit_code": c.get("unit_code"),
+            "address": c.get("address"),
+            "usable_area": c.get("usable_area"),
+            "rooms": c.get("rooms"),
+            "floor": c.get("floor"),
+            "construction_year": c.get("construction_year"),
+            "listing_price": c.get("listing_price"),
+            "selling_price": c.get("selling_price"),
+            "shared_debt": c.get("shared_debt"),
+            "sqm_price": c.get("sqm_price"),
+            "common_costs": c.get("common_costs"),
+            "days_on_market": c.get("days_on_market"),
+            "finalized_at": (c.get("finalized_at") or "")[:10],
+        }
+
+    sorted_comps = sorted(comps, key=lambda c: c.get("finalized_at") or "", reverse=True)
+    slim_comps = [_slim_comp(c) for c in sorted_comps[:15]]
+
+    score = item.get("score") or {}
+    summary = item.get("summary") or {}
+
+    # Keep full score breakdown — 12 dimensions + nearby_transit = ~220 bytes, all signal.
+    # Drop nothing from scores.
+    slim_score = {k: v for k, v in score.items()}
+
+    eiendom: dict | None = None
+    if eu:
+        eiendom = {
+            "unit_code": eu.get("unit_code"),
+            "usable_area": eu.get("usable_area"),
+            "estimated_price": eu.get("estimated_selling_price"),
+            "estimated_range": [
+                eu.get("estimated_selling_price_lower"),
+                eu.get("estimated_selling_price_upper"),
+            ],
+            "listing_sqm_price": eu.get("listing_sqm_price"),
+            "market_placement": eu.get("market_placement_score"),
+            "sale_status": eu.get("sale_status"),
+            "days_on_market": eu.get("days_on_market"),
+            "avg_comp_sqm_price": avg_comp_sqm,
+            "comp_count": len(comps),
+        }
+
+    return {
+        "rank": rank,
+        "finnkode": item.get("finnkode"),
+        "url": item.get("url"),
+        "title": item.get("title"),
+        "address": item.get("address"),
+        "district": item.get("district"),
+        "property_type": item.get("property_type"),
+        "ownership_type": item.get("ownership_type"),
+        "floor": item.get("floor"),
+        "area_m2": item.get("area_m2"),
+        "bedrooms": item.get("bedrooms"),
+        "rooms": item.get("rooms"),
+        "total_price": item.get("total_price"),
+        "asking_price": item.get("asking_price"),
+        "shared_debt": item.get("shared_debt"),
+        "common_costs": item.get("common_costs"),
+        "construction_year": item.get("construction_year"),
+        "has_balcony": item.get("has_balcony"),
+        "has_terrace": item.get("has_terrace"),
+        "has_elevator": item.get("has_elevator"),
+        "has_parking": item.get("has_parking"),
+        "has_garage": item.get("has_garage"),
+        "eiendom_unit_code": item.get("eiendom_unit_code"),
+        "score": slim_score,
+        "categories": item.get("categories"),
+        "why_interesting": summary.get("why_interesting"),
+        "risks": summary.get("risks"),
+        "eiendom": eiendom,
+        "similar_units": slim_comps,
+    }
+
+
+def _build_slim_search_result(full: dict) -> dict:
+    """Convert full analyze_search output to a compact MCP-safe response.
+
+    Removes search_cards (redundant), drops all fat fields from individual
+    listings. Target: <200KB for 30 analyzed ads.
+    """
+    listings = [
+        _slim_listing(rank + 1, item)
+        for rank, item in enumerate(full.get("analysis") or [])
+    ]
+    return {
+        "search_url": full.get("search_url"),
+        "summary": full.get("summary"),
+        "listings": listings,
+    }
+
+
 def _build_transport_security() -> TransportSecuritySettings:
     allowed = os.getenv("MCP_ALLOWED_HOSTS", "")
     if allowed:
@@ -57,10 +175,13 @@ mcp = FastMCP("finn_eiendom_mcp", transport_security=_build_transport_security()
     description=(
         "Analyze a FINN.no real estate search URL. Scrapes listing cards,"
         " fetches details, enriches with Eiendom.no data, scores, and ranks."
+        " Fetches all ads in parallel (phase 1) then scores from cache (phase 2)."
+        " Progress updates are emitted during phase 1."
     )
 )
 async def finn_analyze_search(
     search_url: str,
+    ctx: Context,
     max_pages: int = 3,
     detail_limit: int = 20,
     include_details: bool = True,
@@ -74,8 +195,9 @@ async def finn_analyze_search(
             include_details=include_details,
             detail_limit=detail_limit,
             include_eiendom_no=include_eiendom_no,
+            ctx=ctx,
         )
-        return json.dumps(result, default=str)
+        return json.dumps(_build_slim_search_result(result), default=str)
     except Exception as e:
         logger.error(f"Error analyzing search: {e}")
         return json.dumps({"error": True, "message": str(e)})
@@ -143,17 +265,60 @@ async def finn_get_eiendom_unit(unit_code: str, force_refresh: bool = False) ->
 @mcp.tool(
     description=(
         "Fetch and analyze unit images for visual assessment of a property. "
-        "Returns property photos with metadata for evaluating views, condition, and layout."
+        "Downloads photos and returns them as visual image content so Claude can "
+        "directly assess views, condition, layout, kitchen/bathroom quality, and atmosphere."
     )
 )
-async def finn_analyze_unit_images(unit_code: str, force_refresh: bool = False) -> str:
+async def finn_analyze_unit_images(
-    """Fetch and return unit images for visual analysis."""
+    unit_code: str,
+    force_refresh: bool = False,
+    max_images: int = 8,
+) -> list:
+    """Fetch unit images and return as vision-compatible image content blocks."""
     try:
         result = await get_unit_images(unit_code, force_refresh=force_refresh)
-        return render_unit_images(result, "markdown")
+        all_urls = result.get("unit_images") or []
+        urls = all_urls[:max_images]
+
+        header = (
+            f"{result.get('address', unit_code)} | "
+            f"{result.get('rooms')} rom | "
+            f"{result.get('usable_area')}m² | "
+            f"{len(all_urls)} bilder totalt, viser {len(urls)}"
+        )
+        content: list = [TextContent(type="text", text=header)]
+
+        async def _fetch(url: str) -> ImageContent | None:
+            try:
+                async with httpx.AsyncClient(timeout=15) as client:
+                    resp = await client.get(url)
+                if resp.status_code != 200:
+                    return None
+
+                # Resize to max 1024px on longest side before encoding.
+                # Raw real estate photos are 2-4MB — must compress to stay
+                # within the 1MB MCP tool result limit across multiple images.
+                from PIL import Image
+                import io
+                img = Image.open(io.BytesIO(resp.content))
+                img.thumbnail((1024, 1024), Image.LANCZOS)
+                if img.mode in ("RGBA", "P"):
+                    img = img.convert("RGB")
+                buf = io.BytesIO()
+                img.save(buf, format="JPEG", quality=75, optimize=True)
+                b64 = base64.b64encode(buf.getvalue()).decode()
+                return ImageContent(type="image", data=b64, mimeType="image/jpeg")
+            except Exception as exc:
+                logger.warning("Failed to fetch/resize image %s: %s", url, exc)
+            return None
+
+        fetched = await asyncio.gather(*[_fetch(u) for u in urls])
+        content.extend(img for img in fetched if img is not None)
+        return content
+
     except Exception as e:
         logger.error(f"Error fetching unit images for {unit_code}: {e}")
-        return json.dumps({"error": True, "message": str(e)})
+        return [TextContent(type="text", text=json.dumps({"error": True, "message": str(e)}))]
 
 
 @mcp.tool(

finn_eiendom/service.py

@@ -215,6 +215,7 @@ async def analyze_search(
     detail_limit: int = 20,
     include_details: bool = True,
     include_eiendom_no: bool = True,
+    ctx: Any = None,
 ) -> dict[str, Any]:
     """Analyze a FINN search URL and return a ranked shortlist.
 
@@ -227,6 +228,7 @@ async def analyze_search(
         fetch_details=include_details,
         detail_limit=detail_limit,
         include_eiendom_no=include_eiendom_no,
+        ctx=ctx,
     )
 
 

pyproject.toml

@@ -10,6 +10,7 @@ dependencies = [
   "lxml>=5.0.0",
   "mcp[cli]>=1.0.0",
   "msgpack>=1.0.0",
+  "pillow>=10.0.0",
   "pydantic>=2.8.0",
   "pydantic-settings>=2.4.0",
   "python-dotenv>=1.0.0",