backend updates on recommendation system

backend/app/mcp_server.py

@@ -27,13 +27,15 @@ mcp = FastMCP(
         "Every product is confirmed by real listings on at least two retail platforms; "
         "prices, ratings and reviews come with the page they were read from. Prices are "
         "rupee strings. Use search_products to find products, then get_product for "
-        "per-platform offers, specs, images, rating and reviews."
+        "per-platform offers, specs, images, rating and reviews, and recommend_products "
+        "for similar alternatives."
     ),
 )
 
 _SEARCH_FIELDS = (
     "product_id", "brand", "category", "display_name", "ram_gb", "storage_gb",
     "best_price", "best_price_site", "platform_count", "sold_by_tn_retailer", "image_url",
+    "rating", "rating_count",
 )
 
 
@@ -71,7 +73,8 @@ async def search_products(
         limit: Maximum products to return (1-100).
 
     Returns the total match count and, per product: id, name, variant, best price (rupee
-    string) and the platform offering it, number of platforms, and an image URL (or null).
+    string) and the platform offering it, number of platforms, an image URL (or null), and the
+    overall rating and rating count (null when no platform publishes a rating).
     """
     limit = max(1, min(int(limit), 100))
     result = await _run(
@@ -115,6 +118,36 @@ async def get_product(product_id: int) -> Dict[str, Any]:
     }
 
 
+@mcp.tool
+async def recommend_products(product_id: int, kind: str = "similar", limit: int = 6) -> Dict[str, Any]:
+    """Alternatives to suggest for one product (by its product_id).
+
+    Args:
+        product_id: The product to find alternatives for.
+        kind: "similar" - closest specs, ranked by spec similarity, rating (weighted by how
+            many people rated it) and price closeness; when few exist, the best-rated in the
+            category fill the list. "better_rated" - products rated higher than this one by
+            at least 5 people.
+        limit: Maximum products to return (1-12).
+
+    Always same category, in stock, within a similar price (+/-30% for similar, +/-20% for
+    better_rated), with other variants of the same model left out. Each item has a short
+    reason (e.g. "Similar specs · 4.5★ vs 4.1★"). The same model's other RAM/storage
+    variants are listed separately under other_variants.
+    """
+    if kind not in ("similar", "better_rated"):
+        raise ToolError('kind must be "similar" or "better_rated"')
+    d = await _run(elec.recommendations, int(product_id), kind, max(1, min(int(limit), 12)), False)
+    return {
+        "items": [
+            {k: i.get(k) for k in ("product_id", "brand", "display_name", "ram_gb", "storage_gb",
+                                   "best_price", "best_price_site", "rating", "rating_count", "reason")}
+            for i in d["items"]
+        ],
+        "other_variants": d["other_variants"],
+    }
+
+
 @mcp.tool
 async def price_history(product_id: int) -> List[Dict[str, Any]]:
     """Every price observed for a product, per platform, oldest first (rupee strings, ISO times)."""