diff --git a/src/services/models_service.py b/src/services/models_service.py
index 7620461b..e38c4e8b 100644
--- a/src/services/models_service.py
+++ b/src/services/models_service.py
@@ -8,6 +8,18 @@ logger = get_logger(__name__)
 class ModelsService:
     """Service for fetching available models from different AI providers"""
 
+    OLLAMA_EMBEDDING_MODELS = [
+        "nomic-embed-text",
+        "mxbai-embed-large",
+        "snowflake-arctic-embed",
+        "all-minilm",
+        "bge-m3",
+        "bge-large",
+        "paraphrase-multilingual",
+        "granite-embedding",
+        "jina-embeddings-v2-base-en",
+    ]
+
     def __init__(self):
         self.session_manager = None
 
@@ -80,49 +92,93 @@ class ModelsService:
     async def get_ollama_models(
         self, endpoint: str = None
     ) -> Dict[str, List[Dict[str, str]]]:
-        """Fetch available models from Ollama API"""
+        """Fetch available models from Ollama API with tool calling capabilities for language models"""
         try:
             # Use provided endpoint or default
             ollama_url = endpoint
 
+            # API endpoints
+            tags_url = f"{ollama_url}/api/tags"
+            show_url = f"{ollama_url}/api/show"
+
+            # Constants for JSON parsing
+            JSON_MODELS_KEY = "models"
+            JSON_NAME_KEY = "name"
+            JSON_CAPABILITIES_KEY = "capabilities"
+            DESIRED_CAPABILITY = "completion"
+            TOOL_CALLING_CAPABILITY = "tools"
+
             async with httpx.AsyncClient() as client:
-                response = await client.get(f"{ollama_url}/api/tags", timeout=10.0)
+                # Fetch available models
+                tags_response = await client.get(tags_url, timeout=10.0)
+                tags_response.raise_for_status()
+                models_data = tags_response.json()
 
-            if response.status_code == 200:
-                data = response.json()
-                models = data.get("models", [])
+                logger.debug(f"Available models: {models_data}")
 
-                # Extract model names
+                # Filter models based on capabilities
                 language_models = []
                 embedding_models = []
 
-                for model in models:
-                    model_name = model.get("name", "").split(":")[
-                        0
-                    ]  # Remove tag if present
+                models = models_data.get(JSON_MODELS_KEY, [])
 
-                    if model_name:
-                        # Most Ollama models can be used as language models
-                        language_models.append(
-                            {
-                                "value": model_name,
-                                "label": model_name,
-                                "default": "llama3" in model_name.lower(),
-                            }
+                for model in models:
+                    model_name = model.get(JSON_NAME_KEY, "")
+                    # Remove tag if present (e.g., "llama3:latest" -> "llama3")
+                    clean_model_name = model_name.split(":")[0] if model_name else ""
+
+                    if not clean_model_name:
+                        continue
+
+                    logger.debug(f"Checking model: {model_name}")
+
+                    # Check model capabilities
+                    payload = {"model": model_name}
+                    try:
+                        show_response = await client.post(
+                            show_url, json=payload, timeout=10.0
+                        )
+                        show_response.raise_for_status()
+                        json_data = show_response.json()
+
+                        capabilities = json_data.get(JSON_CAPABILITIES_KEY, [])
+                        logger.debug(
+                            f"Model: {model_name}, Capabilities: {capabilities}"
                         )
 
-                        # Some models are specifically for embeddings
-                        if any(
-                            embed in model_name.lower()
-                            for embed in ["embed", "sentence", "all-minilm"]
-                        ):
+                        # Check if model has required capabilities
+                        has_completion = DESIRED_CAPABILITY in capabilities
+                        has_tools = TOOL_CALLING_CAPABILITY in capabilities
+
+                        # Check if it's an embedding model
+                        is_embedding = any(
+                            embed_model in clean_model_name.lower()
+                            for embed_model in self.OLLAMA_EMBEDDING_MODELS
+                        )
+
+                        if is_embedding:
+                            # Embedding models only need completion capability
                             embedding_models.append(
                                 {
-                                    "value": model_name,
-                                    "label": model_name,
+                                    "value": clean_model_name,
+                                    "label": clean_model_name,
                                     "default": False,
                                 }
                             )
+                        elif not is_embedding and has_completion and has_tools:
+                            # Language models need both completion and tool calling
+                            language_models.append(
+                                {
+                                    "value": clean_model_name,
+                                    "label": clean_model_name,
+                                    "default": "llama3" in clean_model_name.lower(),
+                                }
+                            )
+                    except Exception as e:
+                        logger.warning(
+                            f"Failed to check capabilities for model {model_name}: {str(e)}"
+                        )
+                        continue
 
                 # Remove duplicates and sort
                 language_models = list(
@@ -137,15 +193,14 @@ class ModelsService:
                 )
                 embedding_models.sort(key=lambda x: x["value"])
 
+                logger.info(
+                    f"Found {len(language_models)} language models with tool calling and {len(embedding_models)} embedding models"
+                )
+
                 return {
                     "language_models": language_models,
-                    "embedding_models": embedding_models if embedding_models else [],
+                    "embedding_models": embedding_models,
                 }
-            else:
-                logger.error(f"Failed to fetch Ollama models: {response.status_code}")
-                raise Exception(
-                    f"Ollama API returned status code {response.status_code}"
-                )
 
         except Exception as e:
             logger.error(f"Error fetching Ollama models: {str(e)}")