diff --git a/mcp-server/src/legal_mcp/server.py b/mcp-server/src/legal_mcp/server.py
index 81bfbd7..0e35bee 100644
--- a/mcp-server/src/legal_mcp/server.py
+++ b/mcp-server/src/legal_mcp/server.py
@@ -528,6 +528,16 @@ async def get_style_guide() -> str:
     return await drafting.get_style_guide()
 
 
+@mcp.tool()
+async def style_distance(case_number: str) -> str:
+    """מדד מרחק-סגנון (T7) — האם הטיוטה מתכנסת לסגנון דפנה: סטיית יחסי-זהב,
+    ספירת אנטי-דפוסים, ושיעור-השינוי draft→final מפנקס-ההתאמה. ללא LLM."""
+    import json as _json
+    from legal_mcp.services import style_distance as _sd
+    result = await _sd.style_distance(case_number)
+    return _json.dumps(result, ensure_ascii=False, indent=2)
+
+
 @mcp.tool()
 async def draft_section(
     case_number: str,
diff --git a/mcp-server/src/legal_mcp/services/lessons.py b/mcp-server/src/legal_mcp/services/lessons.py
index df8f949..a91c4d3 100644
--- a/mcp-server/src/legal_mcp/services/lessons.py
+++ b/mcp-server/src/legal_mcp/services/lessons.py
@@ -42,6 +42,25 @@ GOLDEN_RATIOS: dict[str, dict[str, tuple[int, int]]] = {
     "partial_acceptance": {"background": (25, 35), "claims": (25, 30), "discussion": (40, 47), "summary": (2, 3)},
 }
 
+# ── Anti-patterns (what Dafna avoids) — detectable signals for style-distance (T7) ──
+# Derived from daphna-voice-fingerprint.md §3 (corrected 2026-06-06: sequential
+# paragraph numbering is REQUIRED — applied as Word auto-numbering at export — so the
+# anti-pattern is MANUAL numbers typed as text, not numbering itself).
+ANTI_PATTERNS: list[dict] = [
+    {"name": "manual_paragraph_numbers",
+     "regex": r"(?m)^\s*\d{1,3}\.\s",
+     "note": "מספרים ידניים כטקסט בראש פסקה — אמורים להיות auto-numbering בייצוא"},
+    {"name": "inline_numbered_fragments",
+     "regex": r"\([0-9]\)[^\n]{0,200}\([0-9]\)",
+     "note": "פיצול טיעון לרשימת-מיני (1)...(2) בתוך פסקת-אנליזה"},
+    {"name": "markdown_headers",
+     "regex": r"(?m)^#{1,6}\s",
+     "note": "כותרות markdown — אינן בהחלטה הסופית"},
+    {"name": "bullet_lists",
+     "regex": r"(?m)^\s*[-*•]\s",
+     "note": "רשימות תבליטים באנליזה — דפנה כותבת נרטיב רציף"},
+]
+
 # ── Paragraph length guidance (word counts) ────────────────────────
 
 PARAGRAPH_LENGTHS = {
diff --git a/mcp-server/src/legal_mcp/services/style_distance.py b/mcp-server/src/legal_mcp/services/style_distance.py
new file mode 100644
index 0000000..53f9e65
--- /dev/null
+++ b/mcp-server/src/legal_mcp/services/style_distance.py
@@ -0,0 +1,126 @@
+"""מדד מרחק-סגנון (T7) — האם הטיוטות מתכנסות לדפנה לאורך זמן.
+
+שלושה רכיבים, כולם ללא LLM (דטרמיניסטי, זול):
+1. golden_ratio_adherence — סטיית אחוזי-הסעיפים מ-GOLDEN_RATIOS לפי תוצאה.
+2. anti_pattern_hits — ספירת אנטי-דפוסים (מ-lessons.ANTI_PATTERNS) בטקסט הטיוטה.
+3. draft_to_final_diff — change_percent מ-draft_final_pairs (ככל שיורד → מתכנס).
+
+זהו מטא-אות על בריאות-הלמידה (INV-LRN4) — נצרך ע"י לוח-מחוונים / QA, לא ע"י הכותב.
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from uuid import UUID
+
+from legal_mcp.services import db
+from legal_mcp.services.lessons import ANTI_PATTERNS, GOLDEN_RATIOS, canonical_outcome
+
+logger = logging.getLogger(__name__)
+
+# block_id → golden-ratio section
+_BLOCK_TO_SECTION = {
+    "block-vav": "background",
+    "block-zayin": "claims",
+    "block-yod": "discussion",
+    "block-yod-alef": "summary",
+}
+
+
+def count_anti_patterns(text: str) -> dict:
+    """Count each anti-pattern occurrence in text. Lower = closer to Dafna."""
+    hits = {}
+    total = 0
+    for ap in ANTI_PATTERNS:
+        n = len(re.findall(ap["regex"], text or ""))
+        if n:
+            hits[ap["name"]] = {"count": n, "note": ap["note"]}
+            total += n
+    return {"total": total, "by_pattern": hits}
+
+
+def golden_ratio_adherence(block_word_counts: dict[str, int], outcome: str) -> dict:
+    """% of total per section vs GOLDEN_RATIOS target range. deviation=0 ⇒ within range."""
+    outcome = canonical_outcome(outcome)
+    targets = GOLDEN_RATIOS.get(outcome)
+    total = sum(block_word_counts.values())
+    if not targets or total == 0:
+        return {"outcome": outcome, "total_words": total, "sections": {}, "max_deviation": None}
+
+    sections = {}
+    max_dev = 0.0
+    for block_id, section in _BLOCK_TO_SECTION.items():
+        if section not in targets:
+            continue
+        pct = round(block_word_counts.get(block_id, 0) / total * 100, 1)
+        lo, hi = targets[section]
+        if pct < lo:
+            dev = round(lo - pct, 1)
+        elif pct > hi:
+            dev = round(pct - hi, 1)
+        else:
+            dev = 0.0
+        max_dev = max(max_dev, dev)
+        sections[section] = {"actual_pct": pct, "target": [lo, hi], "deviation_pp": dev}
+    return {"outcome": outcome, "total_words": total, "sections": sections, "max_deviation": max_dev}
+
+
+async def style_distance(case_number: str) -> dict:
+    """Assemble the 3 style-distance components for one case (T7)."""
+    case = await db.get_case_by_number(case_number)
+    if not case:
+        return {"error": f"case {case_number} not found"}
+    case_id = UUID(case["id"])
+    decision = await db.get_decision_by_case(case_id)
+    outcome = (decision or {}).get("outcome", "rejection")
+
+    pool = await db.get_pool()
+    async with pool.acquire() as conn:
+        block_rows = []
+        draft_text = ""
+        if decision:
+            block_rows = await conn.fetch(
+                "SELECT block_id, content, word_count FROM decision_blocks "
+                "WHERE decision_id = $1 ORDER BY block_index",
+                UUID(decision["id"]),
+            )
+            draft_text = "\n\n".join(b["content"] for b in block_rows if b["content"])
+        pair = await conn.fetchrow(
+            "SELECT draft_text, diff_stats, status FROM draft_final_pairs "
+            "WHERE case_id = $1 ORDER BY created_at DESC LIMIT 1",
+            case_id,
+        )
+
+    # Prefer the immutable snapshot's draft text when present.
+    if pair and pair["draft_text"]:
+        draft_text = pair["draft_text"]
+
+    word_counts = {b["block_id"]: (b["word_count"] or 0) for b in block_rows}
+    ratios = golden_ratio_adherence(word_counts, outcome)
+    anti = count_anti_patterns(draft_text)
+
+    diff = None
+    if pair and pair["diff_stats"]:
+        raw = pair["diff_stats"]
+        if isinstance(raw, str):
+            import json
+            try:
+                raw = json.loads(raw)
+            except (json.JSONDecodeError, TypeError):
+                raw = None
+        diff = raw
+
+    return {
+        "case_number": case_number,
+        "outcome": canonical_outcome(outcome),
+        "golden_ratio_adherence": ratios,
+        "anti_pattern_hits": anti,
+        "draft_to_final_diff": diff,
+        "pair_status": pair["status"] if pair else None,
+        "summary": {
+            "ratio_max_deviation_pp": ratios.get("max_deviation"),
+            "anti_pattern_total": anti["total"],
+            "change_percent": (diff or {}).get("change_percent") if diff else None,
+        },
+    }