diff --git a/mcp-server/src/legal_mcp/services/block_writer.py b/mcp-server/src/legal_mcp/services/block_writer.py
index ef58afb..a60d830 100644
--- a/mcp-server/src/legal_mcp/services/block_writer.py
+++ b/mcp-server/src/legal_mcp/services/block_writer.py
@@ -501,23 +501,51 @@ async def _build_plans_context(case_id: UUID) -> str:
 
 
 async def _build_precedents_context(case_id: UUID, block_id: str) -> str:
-    """Search for similar precedent paragraphs."""
+    """Search for similar precedent paragraphs from other decisions and case law."""
+    parts = []
     try:
         case = await db.get_case(case_id)
+        case_number = case.get("case_number", "") if case else ""
         subject = case.get("subject", "") if case else ""
         query = f"דיון משפטי בנושא {subject}" if subject else "דיון משפטי ועדת ערר"
         query_emb = await embeddings.embed_query(query)
-        results = await db.search_similar(query_embedding=query_emb, limit=5)
+
+        # Search 1: paragraph_embeddings (from other decisions by Dafna)
+        para_results = await db.search_similar_paragraphs(
+            query_embedding=query_emb, limit=10, block_type="block-yod",
+        )
         # Filter out same case
-        results = [r for r in results if str(r.get("case_id")) != str(case_id)]
-        if results:
-            parts = []
-            for r in results[:3]:
-                parts.append(f"[{r.get('case_number', '?')}, {r.get('section_type', '')}] {r['content'][:400]}")
-            return "\n\n".join(parts)
+        para_results = [r for r in para_results if r.get("case_number", "") != case_number]
+        for r in para_results[:4]:
+            parts.append(
+                f"[החלטת {r.get('case_number', '?')} — {r.get('case_title', '')}, "
+                f"בלוק {r.get('block_type', '')}]\n{r['content'][:500]}"
+            )
+
+        # Search 2: case_law_embeddings (precedent case law)
+        pool = await db.get_pool()
+        async with pool.acquire() as conn:
+            caselaw_rows = await conn.fetch(
+                """SELECT cl.case_number, cl.case_name, cl.court, cl.summary, cl.key_quote,
+                          1 - (cle.embedding <=> $1) AS score
+                   FROM case_law_embeddings cle
+                   JOIN case_law cl ON cl.id = cle.case_law_id
+                   ORDER BY cle.embedding <=> $1
+                   LIMIT 5""",
+                query_emb,
+            )
+        for r in caselaw_rows[:3]:
+            text = r["key_quote"] or r["summary"] or ""
+            if text:
+                parts.append(
+                    f"[פסיקה: {r['case_number']} {r['case_name']} ({r.get('court', '')})] "
+                    f"score={r['score']:.3f}\n{text[:400]}"
+                )
+
     except Exception as e:
         logger.warning("Failed to fetch precedents: %s", e)
-    return "(אין תקדימים)"
+
+    return "\n\n".join(parts) if parts else "(אין תקדימים)"
 
 
 async def _build_style_context() -> str:
diff --git a/mcp-server/src/legal_mcp/services/processor.py b/mcp-server/src/legal_mcp/services/processor.py
index 9c42d69..0cad19a 100644
--- a/mcp-server/src/legal_mcp/services/processor.py
+++ b/mcp-server/src/legal_mcp/services/processor.py
@@ -57,6 +57,18 @@ async def process_document(document_id: UUID, case_id: UUID) -> dict:
             len(classification_result["parties"].get("respondents", [])),
         )
 
+        # Step 1.6: Update case parties if empty
+        if case_id and case:
+            parties = classification_result.get("parties", {})
+            updates = {}
+            if not case.get("appellants") and parties.get("appellants"):
+                updates["appellants"] = parties["appellants"]
+            if not case.get("respondents") and parties.get("respondents"):
+                updates["respondents"] = parties["respondents"]
+            if updates:
+                await db.update_case(case_id, **updates)
+                logger.info("Updated case parties: %s", updates)
+
         # Step 2: Chunk
         logger.info("Chunking document (%d chars)", len(text))
         chunks = chunker.chunk_document(text)