diff --git a/src/mcp_ltspice/raw_parser.py b/src/mcp_ltspice/raw_parser.py
index d32e6aa..3e5e8d2 100644
--- a/src/mcp_ltspice/raw_parser.py
+++ b/src/mcp_ltspice/raw_parser.py
@@ -172,28 +172,14 @@ def _parse_binary_data(
     - Complex AC: frequency (float64) + other vars as (float64, float64) pairs
     """
     if is_complex:
-        # Complex data (AC analysis): freq as double + complex values
-        # freq (8 bytes) + (n_vars-1) * (real + imag) = 8 + (n-1)*16 bytes per point
-        bytes_per_point = 8 + (n_vars - 1) * 16
-        expected_bytes = bytes_per_point * points
+        # Complex data (AC analysis): ALL variables stored as complex128
+        # Each value is (real float64, imag float64) = 16 bytes
+        bytes_per_point = n_vars * 16
+        actual_points = len(data) // bytes_per_point
 
-        if len(data) >= expected_bytes:
-            result = np.zeros((n_vars, points), dtype=np.complex128)
-            offset = 0
-
-            for p in range(points):
-                # Frequency as double (real)
-                result[0, p] = struct.unpack("<d", data[offset:offset + 8])[0]
-                offset += 8
-
-                # Rest are complex (real, imag) pairs
-                for v in range(1, n_vars):
-                    real = struct.unpack("<d", data[offset:offset + 8])[0]
-                    imag = struct.unpack("<d", data[offset + 8:offset + 16])[0]
-                    result[v, p] = complex(real, imag)
-                    offset += 16
-
-            return result
+        # Read as flat complex128 array and reshape
+        flat = np.frombuffer(data[:actual_points * bytes_per_point], dtype=np.complex128)
+        return flat.reshape(actual_points, n_vars).T.copy()
 
     # Real data - detect format from data size
     # Mixed format: time (8 bytes) + other vars (4 bytes each)
diff --git a/src/mcp_ltspice/server.py b/src/mcp_ltspice/server.py
index b16b34e..2622662 100644
--- a/src/mcp_ltspice/server.py
+++ b/src/mcp_ltspice/server.py
@@ -10,6 +10,7 @@ This server provides tools for:
 import json
 from pathlib import Path
 
+import numpy as np
 from fastmcp import FastMCP
 
 from . import __version__
@@ -181,9 +182,9 @@ def get_waveform(
 
     if x_axis is not None:
         sampled = x_axis[::step]
-        # Convert complex to magnitude for frequency domain
-        if sampled.dtype == complex:
-            result["x_axis_data"] = [abs(x) for x in sampled]
+        # For frequency domain, take real part (imag is 0)
+        if np.iscomplexobj(sampled):
+            result["x_axis_data"] = sampled.real.tolist()
         else:
             result["x_axis_data"] = sampled.tolist()
         result["returned_points"] = len(result["x_axis_data"])
@@ -194,15 +195,17 @@ def get_waveform(
         if data is not None:
             sampled = data[::step]
             # Handle complex data (AC analysis)
-            if sampled.dtype == complex:
+            if np.iscomplexobj(sampled):
+                import math
                 result["signals"][name] = {
-                    "magnitude": [abs(x) for x in sampled],
-                    "phase_degrees": [
-                        (180 / 3.14159) * (x.imag / x.real if x.real != 0 else 0)
+                    "magnitude_db": [
+                        20 * math.log10(abs(x)) if abs(x) > 0 else -200
+                        for x in sampled
+                    ],
+                    "phase_degrees": [
+                        math.degrees(math.atan2(x.imag, x.real))
                         for x in sampled
                     ],
-                    "real": [x.real for x in sampled],
-                    "imag": [x.imag for x in sampled],
                 }
             else:
                 result["signals"][name] = {"values": sampled.tolist()}