diff --git a/ltx_director.py b/ltx_director.py
index 1f89b8d..f9fb690 100644
--- a/ltx_director.py
+++ b/ltx_director.py
@@ -477,6 +477,30 @@ def _extract_ollama_generated_text(resp_json: dict) -> str:
     return max(cleaned_candidates, key=len)
 
 
+def _collect_ollama_text_candidates(resp_json: dict) -> list[str]:
+    if not isinstance(resp_json, dict):
+        return []
+
+    candidates = [resp_json.get("response"), resp_json.get("content"), resp_json.get("thinking")]
+    message = resp_json.get("message")
+    if isinstance(message, dict):
+        candidates.extend([
+            message.get("content"),
+            message.get("reasoning_content"),
+            message.get("thinking"),
+        ])
+
+    cleaned = []
+    for candidate in candidates:
+        if isinstance(candidate, str):
+            text = candidate.strip()
+            if "<think>" in text:
+                text = text.split("</think>")[-1].strip()
+            if text:
+                cleaned.append(text)
+    return cleaned
+
+
 def _analysis_text_is_usable(text: str) -> bool:
     text = (text or "").strip()
     if not text:
@@ -555,6 +579,7 @@ async def analyze_character_endpoint(request):
         try:
             async with aiohttp.ClientSession() as session:
                 if provider == "ollama":
+                    debug_candidates = []
                     analysis_images = cleaned_b64_list
                     payload = {
                         "model": model_name,
@@ -578,11 +603,13 @@ async def analyze_character_endpoint(request):
                                         retry_err = await retry_response.text()
                                         return web.json_response({"status": "error", "message": f"Ollama HTTP {retry_response.status}: {retry_err}"})
                                     resp_json = await retry_response.json()
+                                    debug_candidates.extend(_collect_ollama_text_candidates(resp_json))
                                     generated_text = _extract_ollama_generated_text(resp_json)
                             else:
                                 return web.json_response({"status": "error", "message": f"Ollama HTTP {response.status}: {err_txt}"})
                         else:
                             resp_json = await response.json()
+                            debug_candidates.extend(_collect_ollama_text_candidates(resp_json))
                             generated_text = _extract_ollama_generated_text(resp_json)
 
                     if not _analysis_text_is_usable(generated_text):
@@ -603,9 +630,17 @@ async def analyze_character_endpoint(request):
                         async with session.post(f"{base_url}/api/chat", json=chat_payload, timeout=300) as response:
                             if response.status == 200:
                                 resp_json = await response.json()
+                                debug_candidates.extend(_collect_ollama_text_candidates(resp_json))
                                 chat_text = _extract_ollama_generated_text(resp_json)
                                 if _analysis_text_is_usable(chat_text):
                                     generated_text = chat_text
+                    if not _analysis_text_is_usable(generated_text):
+                        return web.json_response({
+                            "status": "error",
+                            "message": "Ollama returned only a truncated analysis response.",
+                            "debug_candidates": debug_candidates,
+                            "description": generated_text,
+                        })
                 else:
                     # OpenAI-compatible vision chat (LM Studio / Custom).
                     content = [{"type": "text", "text": _ANALYZE_PROMPT}]