diff --git a/api/database.py b/api/database.py
index a74b857a..0ce1d4ba 100644
--- a/api/database.py
+++ b/api/database.py
@@ -29,6 +29,7 @@ class Feature(Base):
     steps = Column(JSON, nullable=False)  # Stored as JSON array
     passes = Column(Boolean, default=False, index=True)
     in_progress = Column(Boolean, default=False, index=True)
+    regression_count = Column(Integer, default=0, index=True)  # Track regression test frequency
 
     def to_dict(self) -> dict:
         """Convert feature to dictionary for JSON serialization."""
@@ -41,6 +42,7 @@ def to_dict(self) -> dict:
             "steps": self.steps,
             "passes": self.passes,
             "in_progress": self.in_progress,
+            "regression_count": self.regression_count or 0,
         }
 
 
@@ -73,6 +75,21 @@ def _migrate_add_in_progress_column(engine) -> None:
             conn.commit()
 
 
+def _migrate_add_regression_count_column(engine) -> None:
+    """Add regression_count column to existing databases that don't have it."""
+    from sqlalchemy import text
+
+    with engine.connect() as conn:
+        # Check if column exists
+        result = conn.execute(text("PRAGMA table_info(features)"))
+        columns = [row[1] for row in result.fetchall()]
+
+        if "regression_count" not in columns:
+            # Add the column with default value
+            conn.execute(text("ALTER TABLE features ADD COLUMN regression_count INTEGER DEFAULT 0"))
+            conn.commit()
+
+
 def create_database(project_dir: Path) -> tuple:
     """
     Create database and return engine + session maker.
@@ -87,8 +104,9 @@ def create_database(project_dir: Path) -> tuple:
     engine = create_engine(db_url, connect_args={"check_same_thread": False})
     Base.metadata.create_all(bind=engine)
 
-    # Migrate existing databases to add in_progress column
+    # Migrate existing databases to add new columns
     _migrate_add_in_progress_column(engine)
+    _migrate_add_regression_count_column(engine)
 
     SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
     return engine, SessionLocal
diff --git a/mcp_server/feature_mcp.py b/mcp_server/feature_mcp.py
index 1534bc1b..56962e6b 100755
--- a/mcp_server/feature_mcp.py
+++ b/mcp_server/feature_mcp.py
@@ -28,7 +28,6 @@
 
 from mcp.server.fastmcp import FastMCP
 from pydantic import BaseModel, Field
-from sqlalchemy.sql.expression import func
 
 # Add parent directory to path so we can import from api module
 sys.path.insert(0, str(Path(__file__).parent.parent))
@@ -178,11 +177,14 @@ def feature_get_next() -> str:
 def feature_get_for_regression(
     limit: Annotated[int, Field(default=3, ge=1, le=10, description="Maximum number of passing features to return")] = 3
 ) -> str:
-    """Get random passing features for regression testing.
+    """Get passing features for regression testing, prioritizing least-tested features.
 
-    Returns a random selection of features that are currently passing.
-    Use this to verify that previously implemented features still work
-    after making changes.
+    Returns features that are currently passing, ordered by regression_count (ascending)
+    so that features tested fewer times are prioritized. This ensures even distribution
+    of regression testing across all features, avoiding duplicate testing of the same
+    features while others are never tested.
+
+    Each returned feature has its regression_count incremented to track testing frequency.
 
     Args:
         limit: Maximum number of features to return (1-10, default 3)
@@ -192,14 +194,25 @@ def feature_get_for_regression(
     """
     session = get_session()
     try:
+        # Select features with lowest regression_count first (least tested)
+        # Use id as secondary sort for deterministic ordering when counts are equal
         features = (
             session.query(Feature)
             .filter(Feature.passes == True)
-            .order_by(func.random())
+            .order_by(Feature.regression_count.asc(), Feature.id.asc())
             .limit(limit)
             .all()
         )
 
+        # Increment regression_count for selected features
+        for feature in features:
+            feature.regression_count = (feature.regression_count or 0) + 1
+        session.commit()
+
+        # Refresh to get updated counts
+        for feature in features:
+            session.refresh(feature)
+
         return json.dumps({
             "features": [f.to_dict() for f in features],
             "count": len(features)