diff --git a/.gitignore b/.gitignore
index 049603f1b..76d9f9cd1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,4 @@
-s# Byte-compiled / optimized / DLL files
+# Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
 *$py.class
diff --git a/pyproject.toml b/pyproject.toml
index 39f98ccda..74b7b2d0f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,8 +31,12 @@ dependencies = [
     "a2a-types>=0.1.0",
     "opentelemetry-instrumentation-openai>=0.39.3; python_version >= '3.10' and python_version < '4.0'",
     "opentelemetry-instrumentation-anthropic>=0.39.3; python_version >= '3.10' and python_version < '4.0'",
-    "tensorzero>=2025.4.7",
     "opentelemetry-instrumentation-mcp>=0.40.3; python_version >= '3.10' and python_version < '4.0'",
+    "google-genai",
+    "opentelemetry-instrumentation-google-genai>=0.2b0",
+    "tensorzero>=2025.4.7",
+    "google-genai",
+    "opentelemetry-instrumentation-google-genai>=0.2b0",
 ]
 
 [project.optional-dependencies]
diff --git a/src/mcp_agent/context.py b/src/mcp_agent/context.py
index 28934844d..559647d9b 100644
--- a/src/mcp_agent/context.py
+++ b/src/mcp_agent/context.py
@@ -11,6 +11,7 @@
 from opentelemetry import trace
 from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
 from opentelemetry.instrumentation.anthropic import AnthropicInstrumentor
+from opentelemetry.instrumentation.google_genai import GoogleGenAiSdkInstrumentor
 from opentelemetry.instrumentation.mcp import McpInstrumentor
 from opentelemetry.instrumentation.openai import OpenAIInstrumentor
 from opentelemetry.propagate import set_global_textmap
@@ -112,6 +113,7 @@ async def configure_otel(config: "Settings") -> None:
     trace.set_tracer_provider(tracer_provider)
     AnthropicInstrumentor().instrument()
     OpenAIInstrumentor().instrument()
+    GoogleGenAiSdkInstrumentor().instrument()
     McpInstrumentor().instrument()
 
 
diff --git a/src/mcp_agent/llm/model_factory.py b/src/mcp_agent/llm/model_factory.py
index d0f1d80ac..318dab00b 100644
--- a/src/mcp_agent/llm/model_factory.py
+++ b/src/mcp_agent/llm/model_factory.py
@@ -13,7 +13,8 @@
 from mcp_agent.llm.providers.augmented_llm_azure import AzureOpenAIAugmentedLLM
 from mcp_agent.llm.providers.augmented_llm_deepseek import DeepSeekAugmentedLLM
 from mcp_agent.llm.providers.augmented_llm_generic import GenericAugmentedLLM
-from mcp_agent.llm.providers.augmented_llm_google import GoogleAugmentedLLM
+from mcp_agent.llm.providers.augmented_llm_google_native import GoogleNativeAugmentedLLM
+from mcp_agent.llm.providers.augmented_llm_google_oai import GoogleOaiAugmentedLLM
 from mcp_agent.llm.providers.augmented_llm_openai import OpenAIAugmentedLLM
 from mcp_agent.llm.providers.augmented_llm_openrouter import OpenRouterAugmentedLLM
 from mcp_agent.llm.providers.augmented_llm_tensorzero import TensorZeroAugmentedLLM
@@ -31,6 +32,9 @@
     Type[DeepSeekAugmentedLLM],
     Type[OpenRouterAugmentedLLM],
     Type[TensorZeroAugmentedLLM],
+    Type[GoogleNativeAugmentedLLM],
+    Type[GenericAugmentedLLM],
+    Type[AzureOpenAIAugmentedLLM],
 ]
 
 
@@ -60,10 +64,12 @@ class ModelFactory:
         "high": ReasoningEffort.HIGH,
     }
 
-    # TODO -- add context window size information for display/management
-    # TODO -- add audio supporting got-4o-audio-preview
-    # TODO -- bring model parameter configuration here
-    # Mapping of model names to their default providers
+    """
+    TODO -- add context window size information for display/management
+    TODO -- add audio supporting got-4o-audio-preview
+    TODO -- bring model parameter configuration here
+    Mapping of model names to their default providers
+    """
     DEFAULT_PROVIDERS = {
         "passthrough": Provider.FAST_AGENT,
         "playback": Provider.FAST_AGENT,
@@ -91,7 +97,9 @@ class ModelFactory:
         "claude-sonnet-4-20250514": Provider.ANTHROPIC,
         "claude-sonnet-4-0": Provider.ANTHROPIC,
         "deepseek-chat": Provider.DEEPSEEK,
-        #        "deepseek-reasoner": Provider.DEEPSEEK, reinstate on release
+        "gemini-2.0-flash": Provider.GOOGLE,
+        "gemini-2.5-flash-preview-05-20": Provider.GOOGLE,
+        "gemini-2.5-pro-preview-05-06": Provider.GOOGLE,
     }
 
     MODEL_ALIASES = {
@@ -108,6 +116,9 @@ class ModelFactory:
         "opus3": "claude-3-opus-latest",
         "deepseekv3": "deepseek-chat",
         "deepseek": "deepseek-chat",
+        "gemini2": "gemini-2.0-flash",
+        "gemini25": "gemini-2.5-flash-preview-05-20",
+        "gemini25pro": "gemini-2.5-pro-preview-05-06",
     }
 
     # Mapping of providers to their LLM classes
@@ -117,7 +128,8 @@ class ModelFactory:
         Provider.FAST_AGENT: PassthroughLLM,
         Provider.DEEPSEEK: DeepSeekAugmentedLLM,
         Provider.GENERIC: GenericAugmentedLLM,
-        Provider.GOOGLE: GoogleAugmentedLLM,  # type: ignore
+        Provider.GOOGLE_OAI: GoogleOaiAugmentedLLM,
+        Provider.GOOGLE: GoogleNativeAugmentedLLM,
         Provider.OPENROUTER: OpenRouterAugmentedLLM,
         Provider.TENSORZERO: TensorZeroAugmentedLLM,
         Provider.AZURE: AzureOpenAIAugmentedLLM,
@@ -132,43 +144,60 @@ class ModelFactory:
     @classmethod
     def parse_model_string(cls, model_string: str) -> ModelConfig:
         """Parse a model string into a ModelConfig object"""
-        # Check if model string is an alias
         model_string = cls.MODEL_ALIASES.get(model_string, model_string)
         parts = model_string.split(".")
 
-        # Start with all parts as the model name
-        model_parts = parts.copy()
+        model_name_str = model_string  # Default full string as model name initially
         provider = None
         reasoning_effort = None
+        parts_for_provider_model = []
 
-        # Check last part for reasoning effort
+        # Check for reasoning effort first (last part)
         if len(parts) > 1 and parts[-1].lower() in cls.EFFORT_MAP:
             reasoning_effort = cls.EFFORT_MAP[parts[-1].lower()]
-            model_parts = model_parts[:-1]
+            # Remove effort from parts list for provider/model name determination
+            parts_for_provider_model = parts[:-1]
+        else:
+            parts_for_provider_model = parts[:]
+
+        # Try to match longest possible provider string
+        identified_provider_parts = 0  # How many parts belong to the provider string
+
+        if len(parts_for_provider_model) >= 2:
+            potential_provider_str = f"{parts_for_provider_model[0]}.{parts_for_provider_model[1]}"
+            if any(p.value == potential_provider_str for p in Provider):
+                provider = Provider(potential_provider_str)
+                identified_provider_parts = 2
+
+        if provider is None and len(parts_for_provider_model) >= 1:
+            potential_provider_str = parts_for_provider_model[0]
+            if any(p.value == potential_provider_str for p in Provider):
+                provider = Provider(potential_provider_str)
+                identified_provider_parts = 1
+
+        # Construct model_name from remaining parts
+        if identified_provider_parts > 0:
+            model_name_str = ".".join(parts_for_provider_model[identified_provider_parts:])
+        else:
+            # If no provider prefix was matched, the whole string (after effort removal) is the model name
+            model_name_str = ".".join(parts_for_provider_model)
 
-        # Check first part for provider
-        if len(model_parts) > 1:
-            potential_provider = model_parts[0]
-            if any(provider.value == potential_provider for provider in Provider):
-                provider = Provider(potential_provider)
-                model_parts = model_parts[1:]
+        # If provider still None, try to get from DEFAULT_PROVIDERS using the model_name_str
+        if provider is None:
+            provider = cls.DEFAULT_PROVIDERS.get(model_name_str)
+            if provider is None:
+                raise ModelConfigError(
+                    f"Unknown model or provider for: {model_string}. Model name parsed as '{model_name_str}'"
+                )
 
-        if provider == Provider.TENSORZERO and not model_parts:
+        if provider == Provider.TENSORZERO and not model_name_str:
             raise ModelConfigError(
                 f"TensorZero provider requires a function name after the provider "
                 f"(e.g., tensorzero.my-function), got: {model_string}"
             )
-        # Join remaining parts as model name
-        model_name = ".".join(model_parts)
-
-        # If no provider was found in the string, look it up in defaults
-        if provider is None:
-            provider = cls.DEFAULT_PROVIDERS.get(model_name)
-            if provider is None:
-                raise ModelConfigError(f"Unknown model: {model_name}")
 
         return ModelConfig(
-            provider=provider, model_name=model_name, reasoning_effort=reasoning_effort
+            provider=provider, model_name=model_name_str, reasoning_effort=reasoning_effort
         )
 
     @classmethod
@@ -185,33 +214,37 @@ def create_factory(
         Returns:
             A callable that takes an agent parameter and returns an LLM instance
         """
-        # Parse configuration up front
         config = cls.parse_model_string(model_string)
+
+        # Ensure provider is valid before trying to access PROVIDER_CLASSES with it
+        if (
+            config.provider not in cls.PROVIDER_CLASSES
+            and config.model_name not in cls.MODEL_SPECIFIC_CLASSES
+        ):
+            # This check is important if a provider (like old GOOGLE) is commented out from PROVIDER_CLASSES
+            raise ModelConfigError(
+                f"Provider '{config.provider}' not configured in PROVIDER_CLASSES and model '{config.model_name}' not in MODEL_SPECIFIC_CLASSES."
+            )
+
         if config.model_name in cls.MODEL_SPECIFIC_CLASSES:
             llm_class = cls.MODEL_SPECIFIC_CLASSES[config.model_name]
         else:
+            # This line is now safer due to the check above
             llm_class = cls.PROVIDER_CLASSES[config.provider]
 
-        # Create a factory function matching the updated attach_llm protocol
         def factory(
             agent: Agent, request_params: Optional[RequestParams] = None, **kwargs
         ) -> AugmentedLLMProtocol:
-            # Create base params with parsed model name
             base_params = RequestParams()
-            base_params.model = config.model_name  # Use the parsed model name, not the alias
-
-            # Add reasoning effort if available
+            base_params.model = config.model_name
             if config.reasoning_effort:
                 kwargs["reasoning_effort"] = config.reasoning_effort.value
-
-            # Forward all arguments to LLM constructor
             llm_args = {
                 "agent": agent,
                 "model": config.model_name,
                 "request_params": request_params,
                 **kwargs,
             }
-
             llm: AugmentedLLMProtocol = llm_class(**llm_args)
             return llm
 
diff --git a/src/mcp_agent/llm/provider_types.py b/src/mcp_agent/llm/provider_types.py
index 0e8b94d1a..3e70ec58a 100644
--- a/src/mcp_agent/llm/provider_types.py
+++ b/src/mcp_agent/llm/provider_types.py
@@ -9,11 +9,12 @@ class Provider(Enum):
     """Supported LLM providers"""
 
     ANTHROPIC = "anthropic"
-    OPENAI = "openai"
-    FAST_AGENT = "fast-agent"
-    GOOGLE = "google"
     DEEPSEEK = "deepseek"
+    FAST_AGENT = "fast-agent"
     GENERIC = "generic"
+    GOOGLE_OAI = "googleoai"  # For Google through OpenAI libraries
+    GOOGLE = "google"  # For Google GenAI native library
+    OPENAI = "openai"
     OPENROUTER = "openrouter"
     TENSORZERO = "tensorzero"  # For TensorZero Gateway
     AZURE = "azure"  # Azure OpenAI Service
diff --git a/src/mcp_agent/llm/providers/augmented_llm_google_native.py b/src/mcp_agent/llm/providers/augmented_llm_google_native.py
new file mode 100644
index 000000000..1a7d4b10b
--- /dev/null
+++ b/src/mcp_agent/llm/providers/augmented_llm_google_native.py
@@ -0,0 +1,459 @@
+from typing import List
+
+# Import necessary types and client from google.genai
+from google import genai
+from google.genai import (
+    errors,  # For error handling
+    types,
+)
+from mcp.types import (
+    CallToolRequest,
+    CallToolRequestParams,
+    CallToolResult,
+    EmbeddedResource,
+    ImageContent,
+    TextContent,
+)
+from rich.text import Text
+
+from mcp_agent.core.exceptions import ProviderKeyError
+from mcp_agent.core.prompt import Prompt
+from mcp_agent.core.request_params import RequestParams
+from mcp_agent.llm.augmented_llm import AugmentedLLM
+from mcp_agent.llm.provider_types import Provider
+
+# Import the new converter class
+from mcp_agent.llm.providers.google_converter import GoogleConverter
+from mcp_agent.mcp.prompt_message_multipart import PromptMessageMultipart
+
+# Define default model and potentially other Google-specific defaults
+DEFAULT_GOOGLE_MODEL = "gemini-2.0-flash"
+
+# Suppress this warning for now
+# TODO: Find out where we're passing null
+# warnings.filterwarnings(
+#     "ignore",
+#     message="null is not a valid Type",
+#     category=UserWarning,
+#     module="google.genai._common",
+# )
+
+
+class GoogleNativeAugmentedLLM(AugmentedLLM[types.Content, types.Content]):
+    """
+    Google LLM provider using the native google.genai library.
+    """
+
+    async def _apply_prompt_provider_specific_structured(
+        self,
+        multipart_messages,
+        model,
+        request_params=None,
+    ):
+        """
+        Handles structured output for Gemini models using response_schema and response_mime_type.
+        """
+        import json
+
+        # Prepare request params
+        request_params = self.get_request_params(request_params)
+        # Convert Pydantic model to schema dict for Gemini
+        schema = None
+        try:
+            schema = model.model_json_schema()
+        except Exception:
+            pass
+
+        # Set up Gemini config for structured output
+        def _get_schema_type(model):
+            # Try to get the type annotation for the model (for list[...] etc)
+            # Fallback to dict schema if not available
+            try:
+                return model
+            except Exception:
+                return None
+
+        # Use the schema as a dict or as a type, as Gemini supports both
+        response_schema = _get_schema_type(model)
+        if schema is not None:
+            response_schema = schema
+
+        # Set config for structured output
+        generate_content_config = self._converter.convert_request_params_to_google_config(
+            request_params
+        )
+        generate_content_config.response_mime_type = "application/json"
+        generate_content_config.response_schema = response_schema
+
+        # Convert messages to google.genai format
+        conversation_history = self._converter.convert_to_google_content(multipart_messages)
+
+        # Call Gemini API
+        try:
+            api_response = await self._google_client.aio.models.generate_content(
+                model=request_params.model,
+                contents=conversation_history,
+                config=generate_content_config,
+            )
+        except Exception as e:
+            self.logger.error(f"Error during Gemini structured call: {e}")
+            # Return None and a dummy assistant message
+            return None, Prompt.assistant(f"Error: {e}")
+
+        # Parse the response as JSON and validate against the model
+        if not api_response.candidates or not api_response.candidates[0].content.parts:
+            return None, Prompt.assistant("No structured response returned.")
+
+        # Try to extract the JSON from the first part
+        text = None
+        for part in api_response.candidates[0].content.parts:
+            if part.text:
+                text = part.text
+                break
+        if text is None:
+            return None, Prompt.assistant("No structured text returned.")
+
+        try:
+            json_data = json.loads(text)
+            validated_model = model.model_validate(json_data)
+            # Update LLM history with user and assistant messages for correct history tracking
+            # Add user message(s)
+            for msg in multipart_messages:
+                self.history.append(msg)
+            # Add assistant message
+            assistant_msg = Prompt.assistant(text)
+            self.history.append(assistant_msg)
+            return validated_model, assistant_msg
+        except Exception as e:
+            self.logger.warning(f"Failed to parse structured response: {e}")
+            # Still update history for consistency
+            for msg in multipart_messages:
+                self.history.append(msg)
+            assistant_msg = Prompt.assistant(text)
+            self.history.append(assistant_msg)
+            return None, assistant_msg
+
+    # Define Google-specific parameter exclusions if necessary
+    GOOGLE_EXCLUDE_FIELDS = {
+        # Add fields that should not be passed directly from RequestParams to google.genai config
+        AugmentedLLM.PARAM_MESSAGES,  # Handled by contents
+        AugmentedLLM.PARAM_MODEL,  # Handled during client/call setup
+        AugmentedLLM.PARAM_SYSTEM_PROMPT,  # Handled by system_instruction in config
+        # AugmentedLLM.PARAM_PARALLEL_TOOL_CALLS, # Handled by tool_config in config
+        AugmentedLLM.PARAM_USE_HISTORY,  # Handled by AugmentedLLM base / this class's logic
+        AugmentedLLM.PARAM_MAX_ITERATIONS,  # Handled by this class's loop
+        # Add any other OpenAI-specific params not applicable to google.genai
+    }.union(AugmentedLLM.BASE_EXCLUDE_FIELDS)
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, provider=Provider.GOOGLE, **kwargs)
+        # Initialize the google.genai client
+        self._google_client = self._initialize_google_client()
+        # Initialize the converter
+        self._converter = GoogleConverter()
+
+    def _initialize_google_client(self) -> genai.Client:
+        """
+        Initializes the google.genai client.
+
+        Reads Google API key or Vertex AI configuration from context config.
+        """
+        try:
+            # Example: Authenticate using API key from config
+            api_key = self._api_key()  # Assuming _api_key() exists in base class
+            if not api_key:
+                # Handle case where API key is missing
+                raise ProviderKeyError(
+                    "Google API key not found.", "Please configure your Google API key."
+                )
+
+            # Check for Vertex AI configuration
+            if (
+                self.context
+                and self.context.config
+                and hasattr(self.context.config, "google")
+                and hasattr(self.context.config.google, "vertex_ai")
+                and self.context.config.google.vertex_ai.enabled
+            ):
+                vertex_config = self.context.config.google.vertex_ai
+                return genai.Client(
+                    vertexai=True,
+                    project=vertex_config.project_id,
+                    location=vertex_config.location,
+                    # Add other Vertex AI specific options if needed
+                    # http_options=types.HttpOptions(api_version='v1') # Example for v1 API
+                )
+            else:
+                # Default to Gemini Developer API
+                return genai.Client(
+                    api_key=api_key,
+                    # http_options=types.HttpOptions(api_version='v1') # Example for v1 API
+                )
+        except Exception as e:
+            # Catch potential initialization errors and raise ProviderKeyError
+            raise ProviderKeyError("Failed to initialize Google GenAI client.", str(e)) from e
+
+    def _initialize_default_params(self, kwargs: dict) -> RequestParams:
+        """Initialize Google-specific default parameters."""
+        chosen_model = kwargs.get("model", DEFAULT_GOOGLE_MODEL)
+
+        return RequestParams(
+            model=chosen_model,
+            systemPrompt=self.instruction,  # System instruction will be mapped in _google_completion
+            parallel_tool_calls=True,  # Assume parallel tool calls are supported by default with native API
+            max_iterations=20,
+            use_history=True,
+            # Include other relevant default parameters
+        )
+
+    async def _google_completion(
+        self,
+        request_params: RequestParams | None = None,
+    ) -> List[TextContent | ImageContent | EmbeddedResource]:
+        """
+        Process a query using Google's generate_content API and available tools.
+        """
+        request_params = self.get_request_params(request_params=request_params)
+        responses: List[TextContent | ImageContent | EmbeddedResource] = []
+
+        # Load full conversation history if use_history is true
+        if request_params.use_history:
+            # Get history from self.history and convert to google.genai format
+            conversation_history = self._converter.convert_to_google_content(
+                self.history.get(include_completion_history=True)
+            )
+        else:
+            # If not using history, start with an empty list
+            conversation_history = []
+
+        self.logger.debug(f"Google completion requested with messages: {conversation_history}")
+        self._log_chat_progress(
+            self.chat_turn(), model=request_params.model
+        )  # Log chat progress at the start of completion
+
+        # Keep track of the number of messages in history before this turn
+        initial_history_length = len(conversation_history)
+
+        for i in range(request_params.max_iterations):
+            # 1. Get available tools
+            aggregator_response = await self.aggregator.list_tools()
+            available_tools = self._converter.convert_to_google_tools(
+                aggregator_response.tools
+            )  # Convert fast-agent tools to google.genai tools
+
+            # 2. Prepare generate_content arguments
+            generate_content_config = self._converter.convert_request_params_to_google_config(
+                request_params
+            )
+
+            # Add tools and tool_config to generate_content_config if tools are available
+            if available_tools:
+                generate_content_config.tools = available_tools
+                # Set tool_config mode to AUTO to allow the model to decide when to call tools
+                generate_content_config.tool_config = types.ToolConfig(
+                    function_calling_config=types.FunctionCallingConfig(mode="AUTO")
+                )
+
+            # 3. Call the google.genai API
+            try:
+                # Use the async client
+                api_response = await self._google_client.aio.models.generate_content(
+                    model=request_params.model,
+                    contents=conversation_history,  # Pass the current turn's conversation history
+                    config=generate_content_config,
+                )
+                self.logger.debug("Google generate_content response:", data=api_response)
+
+            except errors.APIError as e:
+                # Handle specific Google API errors
+                self.logger.error(f"Google API Error: {e.code} - {e.message}")
+                raise ProviderKeyError(f"Google API Error: {e.code}", e.message) from e
+            except Exception as e:
+                self.logger.error(f"Error during Google generate_content call: {e}")
+                # Decide how to handle other exceptions - potentially re-raise or return an error message
+                raise e
+
+            # 4. Process the API response
+            if not api_response.candidates:
+                # No response from the model, we're done
+                self.logger.debug(f"Iteration {i}: No candidates returned.")
+                break
+
+            candidate = api_response.candidates[0]  # Process the first candidate
+
+            # Convert the model's response content to fast-agent types
+            model_response_content_parts = self._converter.convert_from_google_content(
+                candidate.content
+            )
+
+            # Add model's response to conversation history for potential next turn
+            # This is for the *internal* conversation history of this completion call
+            # to handle multi-turn tool use within one _google_completion call.
+            conversation_history.append(candidate.content)
+
+            # Extract and process text content and tool calls
+            assistant_message_parts = []
+            tool_calls_to_execute = []
+
+            for part in model_response_content_parts:
+                if isinstance(part, TextContent):
+                    responses.append(part)  # Add text content to the final responses to be returned
+                    assistant_message_parts.append(
+                        part
+                    )  # Collect text for potential assistant message display
+                elif isinstance(part, CallToolRequestParams):
+                    # This is a function call requested by the model
+                    tool_calls_to_execute.append(part)  # Collect tool calls to execute
+
+            # Display assistant message if there is text content
+            if assistant_message_parts:
+                # Combine text parts for display
+                assistant_text = "".join(
+                    [p.text for p in assistant_message_parts if isinstance(p, TextContent)]
+                )
+                # Display the assistant message. If there are tool calls, indicate that.
+                if tool_calls_to_execute:
+                    tool_names = ", ".join([tc.name for tc in tool_calls_to_execute])
+                    display_text = Text(
+                        f"{assistant_text}\nAssistant requested tool calls: {tool_names}",
+                        style="dim green italic",
+                    )
+                    await self.show_assistant_message(display_text, tool_names)
+                else:
+                    await self.show_assistant_message(Text(assistant_text))
+
+            # 5. Handle tool calls if any
+            if tool_calls_to_execute:
+                tool_results = []
+                for tool_call_params in tool_calls_to_execute:
+                    # Convert to CallToolRequest and execute
+                    tool_call_request = CallToolRequest(
+                        method="tools/call", params=tool_call_params
+                    )
+                    self.show_tool_call(
+                        aggregator_response.tools,  # Pass fast-agent tool definitions for display
+                        tool_call_request.params.name,
+                        str(
+                            tool_call_request.params.arguments
+                        ),  # Convert dict to string for display
+                    )
+
+                    # Execute the tool call. google.genai does not provide a tool_call_id, pass None.
+                    result = await self.call_tool(tool_call_request, None)
+                    self.show_oai_tool_result(
+                        str(result.content)
+                    )  # Use show_oai_tool_result for consistency
+
+                    tool_results.append((tool_call_params.name, result))  # Store name and result
+
+                    # Add tool result content to the overall responses to be returned
+                    responses.extend(result.content)
+
+                # Convert tool results back to google.genai format and add to conversation_history for the next turn
+                tool_response_google_contents = self._converter.convert_function_results_to_google(
+                    tool_results
+                )
+                conversation_history.extend(tool_response_google_contents)
+
+                self.logger.debug(f"Iteration {i}: Tool call results processed.")
+            else:
+                # If no tool calls, check finish reason to stop or continue
+                # google.genai finish reasons: STOP, MAX_TOKENS, SAFETY, RECITATION, OTHER
+                if candidate.finish_reason in ["STOP", "MAX_TOKENS", "SAFETY"]:
+                    self.logger.debug(
+                        f"Iteration {i}: Stopping because finish_reason is '{candidate.finish_reason}'"
+                    )
+                    # Display message if stopping due to max tokens
+                    if (
+                        candidate.finish_reason == "MAX_TOKENS"
+                        and request_params
+                        and request_params.maxTokens is not None
+                    ):
+                        message_text = Text(
+                            f"the assistant has reached the maximum token limit ({request_params.maxTokens})",
+                            style="dim green italic",
+                        )
+                        await self.show_assistant_message(message_text)
+                    break  # Exit the loop if a stopping condition is met
+                # If no tool calls and no explicit stopping reason, the model might be done.
+                # Break to avoid infinite loops if the model doesn't explicitly stop or call tools.
+                self.logger.debug(
+                    f"Iteration {i}: No tool calls and no explicit stop reason, breaking."
+                )
+                break
+
+        # 6. Update history after all iterations are done (or max_iterations reached)
+        # Only add the new messages generated during this completion turn to history
+        if request_params.use_history:
+            new_google_messages = conversation_history[initial_history_length:]
+            new_multipart_messages = self._converter.convert_from_google_content_list(
+                new_google_messages
+            )
+            self.history.extend(new_multipart_messages)
+
+        self._log_chat_finished(model=request_params.model)  # Use model from request_params
+        return responses  # Return the accumulated responses (fast-agent content types)
+
+    async def _apply_prompt_provider_specific(
+        self,
+        multipart_messages: List[PromptMessageMultipart],
+        request_params: RequestParams | None = None,
+        is_template: bool = False,
+    ) -> PromptMessageMultipart:
+        """
+        Applies the prompt messages and potentially calls the LLM for completion.
+        """
+        request_params = self.get_request_params(
+            request_params=request_params
+        )  # Get request params
+
+        # Add incoming messages to history before calling completion
+        # This ensures the current user message is part of the history for the API call
+        self.history.extend(multipart_messages, is_prompt=is_template)
+
+        last_message_role = multipart_messages[-1].role if multipart_messages else None
+
+        if last_message_role == "user":
+            # If the last message is from the user, call the LLM for a response
+            # _google_completion will now load history internally
+            responses = await self._google_completion(request_params=request_params)
+
+            # History update is now handled within _google_completion
+            pass
+
+            return Prompt.assistant(*responses)  # Return combined responses as an assistant message
+        else:
+            # If the last message is not from the user (e.g., assistant), no completion is needed for this step
+            # The messages have already been added to history by the calling code/framework
+            return multipart_messages[-1]  # Return the last message as is
+
+    async def pre_tool_call(self, tool_call_id: str | None, request: CallToolRequest):
+        """
+        Hook called before a tool call.
+
+        Args:
+            tool_call_id: The ID of the tool call.
+            request: The CallToolRequest object.
+
+        Returns:
+            The modified CallToolRequest object.
+        """
+        # Currently a pass-through, can add Google-specific logic if needed
+        return request
+
+    async def post_tool_call(
+        self, tool_call_id: str | None, request: CallToolRequest, result: CallToolResult
+    ):
+        """
+        Hook called after a tool call.
+
+        Args:
+            tool_call_id: The ID of the tool call.
+            request: The original CallToolRequest object.
+            result: The CallToolResult object.
+
+        Returns:
+            The modified CallToolResult object.
+        """
+        # Currently a pass-through, can add Google-specific logic if needed
+        return result
diff --git a/src/mcp_agent/llm/providers/augmented_llm_google.py b/src/mcp_agent/llm/providers/augmented_llm_google_oai.py
similarity index 89%
rename from src/mcp_agent/llm/providers/augmented_llm_google.py
rename to src/mcp_agent/llm/providers/augmented_llm_google_oai.py
index fc9831eba..7517fb6d0 100644
--- a/src/mcp_agent/llm/providers/augmented_llm_google.py
+++ b/src/mcp_agent/llm/providers/augmented_llm_google_oai.py
@@ -6,9 +6,9 @@
 DEFAULT_GOOGLE_MODEL = "gemini-2.0-flash"
 
 
-class GoogleAugmentedLLM(OpenAIAugmentedLLM):
+class GoogleOaiAugmentedLLM(OpenAIAugmentedLLM):
     def __init__(self, *args, **kwargs) -> None:
-        super().__init__(*args, provider=Provider.GOOGLE, **kwargs)
+        super().__init__(*args, provider=Provider.GOOGLE_OAI, **kwargs)
 
     def _initialize_default_params(self, kwargs: dict) -> RequestParams:
         """Initialize Google OpenAI Compatibility default parameters"""
diff --git a/src/mcp_agent/llm/providers/google_converter.py b/src/mcp_agent/llm/providers/google_converter.py
new file mode 100644
index 000000000..ce5a45815
--- /dev/null
+++ b/src/mcp_agent/llm/providers/google_converter.py
@@ -0,0 +1,361 @@
+import base64
+from typing import Any, Dict, List, Tuple
+
+# Import necessary types from google.genai
+from google.genai import types
+from mcp.types import (
+    BlobResourceContents,
+    CallToolRequest,
+    CallToolRequestParams,
+    CallToolResult,
+    EmbeddedResource,
+    ImageContent,
+    TextContent,
+)
+
+from mcp_agent.core.request_params import RequestParams
+from mcp_agent.mcp.helpers.content_helpers import (
+    get_image_data,
+    get_text,
+    is_image_content,
+    is_resource_content,
+    is_text_content,
+)
+from mcp_agent.mcp.prompt_message_multipart import PromptMessageMultipart
+from mcp_agent.tools.tool_definition import ToolDefinition
+
+
+class GoogleConverter:
+    """
+    Converts between fast-agent and google.genai data structures.
+    """
+
+    def _clean_schema_for_google(self, schema: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Recursively removes unsupported JSON schema keywords for google.genai.types.Schema.
+        Specifically removes 'additionalProperties', '$schema', 'exclusiveMaximum', and 'exclusiveMinimum'.
+        """
+        cleaned_schema = {}
+        unsupported_keys = {
+            "additionalProperties",
+            "$schema",
+            "exclusiveMaximum",
+            "exclusiveMinimum",
+        }
+        supported_string_formats = {"enum", "date-time"}
+
+        for key, value in schema.items():
+            if key in unsupported_keys:
+                continue  # Skip this key
+
+            if (
+                key == "format"
+                and schema.get("type") == "string"
+                and value not in supported_string_formats
+            ):
+                continue  # Remove unsupported string formats
+
+            if isinstance(value, dict):
+                cleaned_schema[key] = self._clean_schema_for_google(value)
+            elif isinstance(value, list):
+                cleaned_schema[key] = [
+                    self._clean_schema_for_google(item) if isinstance(item, dict) else item
+                    for item in value
+                ]
+            else:
+                cleaned_schema[key] = value
+        return cleaned_schema
+
+    def convert_to_google_content(
+        self, messages: List[PromptMessageMultipart]
+    ) -> List[types.Content]:
+        """
+        Converts a list of fast-agent PromptMessageMultipart to google.genai types.Content.
+        Handles different roles and content types (text, images, etc.).
+        """
+        google_contents: List[types.Content] = []
+        for message in messages:
+            parts: List[types.Part] = []
+            for part_content in message.content:  # renamed part to part_content to avoid conflict
+                if is_text_content(part_content):
+                    parts.append(types.Part.from_text(text=get_text(part_content) or ""))
+                elif is_image_content(part_content):
+                    assert isinstance(part_content, ImageContent)
+                    image_bytes = base64.b64decode(get_image_data(part_content) or "")
+                    parts.append(
+                        types.Part.from_bytes(mime_type=part_content.mimeType, data=image_bytes)
+                    )
+                elif is_resource_content(part_content):
+                    assert isinstance(part_content, EmbeddedResource)
+                    if (
+                        "application/pdf" == part_content.resource.mimeType
+                        and hasattr(part_content.resource, "blob")
+                        and isinstance(part_content.resource, BlobResourceContents)
+                    ):
+                        pdf_bytes = base64.b64decode(part_content.resource.blob)
+                        parts.append(
+                            types.Part.from_bytes(
+                                mime_type=part_content.resource.mimeType or "application/pdf",
+                                data=pdf_bytes,
+                            )
+                        )
+                    else:
+                        # Check if the resource itself has text content
+                        resource_text = None
+                        if hasattr(part_content.resource, "text"):  # Direct text attribute
+                            resource_text = part_content.resource.text
+                        # Example: if EmbeddedResource wraps a TextContent-like object in its 'resource' field
+                        elif (
+                            hasattr(part_content.resource, "type")
+                            and part_content.resource.type == "text"
+                            and hasattr(part_content.resource, "text")
+                        ):
+                            resource_text = get_text(part_content.resource)
+
+                        if resource_text is not None:
+                            parts.append(types.Part.from_text(text=resource_text))
+                        else:
+                            # Fallback for other binary types or types without direct text
+                            uri_str = (
+                                part_content.resource.uri
+                                if hasattr(part_content.resource, "uri")
+                                else "unknown_uri"
+                            )
+                            mime_str = (
+                                part_content.resource.mimeType
+                                if hasattr(part_content.resource, "mimeType")
+                                else "unknown_mime"
+                            )
+                            parts.append(
+                                types.Part.from_text(
+                                    text=f"[Resource: {uri_str}, MIME: {mime_str}]"
+                                )
+                            )
+
+            if parts:
+                google_role = (
+                    "user"
+                    if message.role == "user"
+                    else ("model" if message.role == "assistant" else "tool")
+                )
+                google_contents.append(types.Content(role=google_role, parts=parts))
+        return google_contents
+
+    def convert_to_google_tools(self, tools: List[ToolDefinition]) -> List[types.Tool]:
+        """
+        Converts a list of fast-agent ToolDefinition to google.genai types.Tool.
+        """
+        google_tools: List[types.Tool] = []
+        for tool in tools:
+            cleaned_input_schema = self._clean_schema_for_google(tool.inputSchema)
+            function_declaration = types.FunctionDeclaration(
+                name=tool.name,
+                description=tool.description if tool.description else "",
+                parameters=types.Schema(**cleaned_input_schema),
+            )
+            google_tools.append(types.Tool(function_declarations=[function_declaration]))
+        return google_tools
+
+    def convert_from_google_content(
+        self, content: types.Content
+    ) -> List[TextContent | ImageContent | EmbeddedResource | CallToolRequestParams]:
+        """
+        Converts google.genai types.Content from a model response to a list of
+        fast-agent content types or tool call requests.
+        """
+        fast_agent_parts: List[
+            TextContent | ImageContent | EmbeddedResource | CallToolRequestParams
+        ] = []
+        for part in content.parts:
+            if part.text:
+                fast_agent_parts.append(TextContent(type="text", text=part.text))
+            elif part.function_call:
+                fast_agent_parts.append(
+                    CallToolRequestParams(
+                        name=part.function_call.name,
+                        arguments=part.function_call.args,
+                    )
+                )
+        return fast_agent_parts
+
+    def convert_from_google_function_call(
+        self, function_call: types.FunctionCall
+    ) -> CallToolRequest:
+        """
+        Converts a single google.genai types.FunctionCall to a fast-agent CallToolRequest.
+        """
+        return CallToolRequest(
+            method="tools/call",
+            params=CallToolRequestParams(
+                name=function_call.name,
+                arguments=function_call.args,
+            ),
+        )
+
+    def convert_function_results_to_google(
+        self, tool_results: List[Tuple[str, CallToolResult]]
+    ) -> List[types.Content]:
+        """
+        Converts a list of fast-agent tool results to google.genai types.Content
+        with role 'tool'. Handles multimodal content in tool results.
+        """
+        google_tool_response_contents: List[types.Content] = []
+        for tool_name, tool_result in tool_results:
+            current_content_parts: List[types.Part] = []
+            textual_outputs: List[str] = []
+            media_parts: List[types.Part] = []
+
+            for item in tool_result.content:
+                if is_text_content(item):
+                    textual_outputs.append(get_text(item) or "")  # Ensure no None is added
+                elif is_image_content(item):
+                    assert isinstance(item, ImageContent)
+                    try:
+                        image_bytes = base64.b64decode(get_image_data(item) or "")
+                        media_parts.append(
+                            types.Part.from_bytes(data=image_bytes, mime_type=item.mimeType)
+                        )
+                    except Exception as e:
+                        textual_outputs.append(f"[Error processing image from tool result: {e}]")
+                elif is_resource_content(item):
+                    assert isinstance(item, EmbeddedResource)
+                    if (
+                        "application/pdf" == item.resource.mimeType
+                        and hasattr(item.resource, "blob")
+                        and isinstance(item.resource, BlobResourceContents)
+                    ):
+                        try:
+                            pdf_bytes = base64.b64decode(item.resource.blob)
+                            media_parts.append(
+                                types.Part.from_bytes(
+                                    data=pdf_bytes,
+                                    mime_type=item.resource.mimeType or "application/pdf",
+                                )
+                            )
+                        except Exception as e:
+                            textual_outputs.append(f"[Error processing PDF from tool result: {e}]")
+                    else:
+                        # Check if the resource itself has text content
+                        resource_text = None
+                        if hasattr(item.resource, "text"):  # Direct text attribute
+                            resource_text = item.resource.text
+                        # Example: if EmbeddedResource wraps a TextContent-like object in its 'resource' field
+                        elif (
+                            hasattr(item.resource, "type")
+                            and item.resource.type == "text"
+                            and hasattr(item.resource, "text")
+                        ):
+                            resource_text = get_text(item.resource)
+
+                        if resource_text is not None:
+                            textual_outputs.append(resource_text)
+                        else:
+                            uri_str = (
+                                item.resource.uri
+                                if hasattr(item.resource, "uri")
+                                else "unknown_uri"
+                            )
+                            mime_str = (
+                                item.resource.mimeType
+                                if hasattr(item.resource, "mimeType")
+                                else "unknown_mime"
+                            )
+                            textual_outputs.append(
+                                f"[Unhandled Resource in Tool: {uri_str}, MIME: {mime_str}]"
+                            )
+                # Add handling for other content types if needed, for now they are skipped or become unhandled resource text
+
+            function_response_payload: Dict[str, Any] = {"tool_name": tool_name}
+            if textual_outputs:
+                function_response_payload["text_content"] = "\n".join(textual_outputs)
+
+            # Only add media_parts if there are some, otherwise Gemini might error on empty parts for function response
+            if media_parts:
+                # Create the main FunctionResponse part
+                fn_response_part = types.Part.from_function_response(
+                    name=tool_name, response=function_response_payload
+                )
+                current_content_parts.append(fn_response_part)
+                current_content_parts.extend(
+                    media_parts
+                )  # Add media parts after the main response part
+            else:  # If no media parts, the textual output (if any) is the sole content of the function response
+                fn_response_part = types.Part.from_function_response(
+                    name=tool_name, response=function_response_payload
+                )
+                current_content_parts.append(fn_response_part)
+
+            google_tool_response_contents.append(
+                types.Content(role="tool", parts=current_content_parts)
+            )
+        return google_tool_response_contents
+
+    def convert_request_params_to_google_config(
+        self, request_params: RequestParams
+    ) -> types.GenerateContentConfig:
+        """
+        Converts fast-agent RequestParams to google.genai types.GenerateContentConfig.
+        """
+        config_args: Dict[str, Any] = {}
+        if request_params.temperature is not None:
+            config_args["temperature"] = request_params.temperature
+        if request_params.maxTokens is not None:
+            config_args["max_output_tokens"] = request_params.maxTokens
+        if hasattr(request_params, "topK") and request_params.topK is not None:
+            config_args["top_k"] = request_params.topK
+        if hasattr(request_params, "topP") and request_params.topP is not None:
+            config_args["top_p"] = request_params.topP
+        if hasattr(request_params, "stopSequences") and request_params.stopSequences is not None:
+            config_args["stop_sequences"] = request_params.stopSequences
+        if (
+            hasattr(request_params, "presencePenalty")
+            and request_params.presencePenalty is not None
+        ):
+            config_args["presence_penalty"] = request_params.presencePenalty
+        if (
+            hasattr(request_params, "frequencyPenalty")
+            and request_params.frequencyPenalty is not None
+        ):
+            config_args["frequency_penalty"] = request_params.frequencyPenalty
+        if request_params.systemPrompt is not None:
+            config_args["system_instruction"] = request_params.systemPrompt
+        return types.GenerateContentConfig(**config_args)
+
+    def convert_from_google_content_list(
+        self, contents: List[types.Content]
+    ) -> List[PromptMessageMultipart]:
+        """
+        Converts a list of google.genai types.Content to a list of fast-agent PromptMessageMultipart.
+        """
+        return [self._convert_from_google_content(content) for content in contents]
+
+    def _convert_from_google_content(self, content: types.Content) -> PromptMessageMultipart:
+        """
+        Converts a single google.genai types.Content to a fast-agent PromptMessageMultipart.
+        """
+        if content.role == "model" and any(part.function_call for part in content.parts):
+            return PromptMessageMultipart(role="assistant", content=[])
+
+        fast_agent_parts: List[
+            TextContent | ImageContent | EmbeddedResource | CallToolRequestParams
+        ] = []
+        for part in content.parts:
+            if part.text:
+                fast_agent_parts.append(TextContent(type="text", text=part.text))
+            elif part.function_response:
+                response_text = str(part.function_response.response)
+                fast_agent_parts.append(TextContent(type="text", text=response_text))
+            elif part.file_data:
+                fast_agent_parts.append(
+                    EmbeddedResource(
+                        type="resource",
+                        resource=TextContent(
+                            uri=part.file_data.file_uri,
+                            mimeType=part.file_data.mime_type,
+                            text=f"[Resource: {part.file_data.file_uri}, MIME: {part.file_data.mime_type}]",
+                        ),
+                    )
+                )
+
+        fast_agent_role = "user" if content.role == "user" else "assistant"
+        return PromptMessageMultipart(role=fast_agent_role, content=fast_agent_parts)
diff --git a/src/mcp_agent/mcp_server_registry.py b/src/mcp_agent/mcp_server_registry.py
index ddea78f58..621bda9b7 100644
--- a/src/mcp_agent/mcp_server_registry.py
+++ b/src/mcp_agent/mcp_server_registry.py
@@ -70,9 +70,15 @@ def __init__(self, config: Settings | None = None, config_path: str | None = Non
             config (Settings): The Settings object containing the server configurations.
             config_path (str): Path to the YAML configuration file.
         """
-        self.registry = (
-            self.load_registry_from_file(config_path) if config is None else config.mcp.servers
-        )
+        if config is None:
+            self.registry = self.load_registry_from_file(config_path)
+        elif config.mcp is not None and hasattr(config.mcp, 'servers') and config.mcp.servers is not None:
+            # Ensure config.mcp exists, has a 'servers' attribute, and it's not None
+            self.registry = config.mcp.servers
+        else:
+            # Default to an empty dictionary if config.mcp is None or has no 'servers'
+            self.registry = {}
+
         self.init_hooks: Dict[str, InitHookCallable] = {}
         self.connection_manager = MCPConnectionManager(self)
 
@@ -88,8 +94,13 @@ def load_registry_from_file(
         Raises:
             ValueError: If the configuration is invalid.
         """
+        servers = {} 
 
-        servers = get_settings(config_path).mcp.servers or {}
+        settings = get_settings(config_path)
+        
+        if settings.mcp is not None and hasattr(settings.mcp, 'servers') and settings.mcp.servers is not None:
+            return settings.mcp.servers
+        
         return servers
 
     @asynccontextmanager
diff --git a/src/mcp_agent/tools/tool_definition.py b/src/mcp_agent/tools/tool_definition.py
new file mode 100644
index 000000000..412877d43
--- /dev/null
+++ b/src/mcp_agent/tools/tool_definition.py
@@ -0,0 +1,14 @@
+from dataclasses import dataclass, field
+from typing import Any, Dict, Optional
+
+
+@dataclass
+class ToolDefinition:
+    """
+    Represents a definition of a tool available to the agent.
+    """
+
+    name: str
+    description: Optional[str] = None
+    inputSchema: Dict[str, Any] = field(default_factory=dict)
+    # Add other relevant fields if necessary based on how tools are defined in fast-agent
diff --git a/tests/e2e/multimodal/test_multimodal_images.py b/tests/e2e/multimodal/test_multimodal_images.py
index 4dba67035..58af8b90d 100644
--- a/tests/e2e/multimodal/test_multimodal_images.py
+++ b/tests/e2e/multimodal/test_multimodal_images.py
@@ -18,6 +18,7 @@
     [
         "gpt-4.1-mini",  # OpenAI model
         "sonnet",  # Anthropic model
+        "gemini25",  # Not yet turned on as it runs into token limits.
         "azure.gpt-4.1",
     ],
 )
@@ -27,7 +28,7 @@ async def test_agent_with_image_prompt(fast_agent, model_name):
 
     # Define the agent
     @fast.agent(
-        "agent",
+        "default",
         instruction="You are a helpful AI Agent",
         model=model_name,
     )
@@ -53,6 +54,7 @@ async def agent_function():
         "gpt-4.1-mini",  # OpenAI model
         "sonnet",  # Anthropic model
         "azure.gpt-4.1",
+        "gemini25",
         #    "gemini2",
     ],
 )
@@ -62,8 +64,8 @@ async def test_agent_with_mcp_image(fast_agent, model_name):
 
     # Define the agent
     @fast.agent(
-        "agent",
-        instruction="You are a helpful AI Agent",
+        "default",
+        instruction="You are a helpful AI Agent. Do not ask any questions.",
         servers=["image_server"],
         model=model_name,
     )
@@ -72,7 +74,41 @@ async def agent_function():
             # Send the prompt and get the response
 
             response = await agent.send(
-                "Use the image fetch tool to get the sample image and tell me what user name contained in this image?"
+                "Use the image fetch tool to get the sample image and tell me the user name contained in this image?"
+            )
+            assert "evalstate" in response
+
+    await agent_function()
+
+
+@pytest.mark.integration
+@pytest.mark.asyncio
+@pytest.mark.e2e
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "gemini25",  # Google Gemini model -> Works sometimes, but not always. DONE.
+        # And Gemini 2.5 only works with a prompt that is a bit more specific.
+        #    "gemini2",
+    ],
+)
+async def test_agent_with_mcp_image_google(fast_agent, model_name):
+    """Test that the agent can process an image and respond appropriately."""
+    fast = fast_agent
+
+    # Define the agent
+    @fast.agent(
+        "default",
+        instruction="You are a helpful AI Agent.",
+        servers=["image_server"],
+        model=model_name,
+    )
+    async def agent_function():
+        async with fast.run() as agent:
+            # Send the prompt and get the response
+
+            response = await agent.send(
+                "Use the image fetch tool to get the sample image. Then tell me the user name contained in this image."
             )
             assert "evalstate" in response
 
@@ -87,16 +123,16 @@ async def agent_function():
     [
         "gpt-4.1-mini",  # OpenAI model
         "haiku35",  # Anthropic model
+        #    "gemini25",  # This currently uses the OpenAI format. Google Gemini cannot process PDFs with the OpenAI format. It can only do so with the native Gemini format.
     ],
 )
 async def test_agent_with_mcp_pdf(fast_agent, model_name):
-    """Test that the agent can process an image and respond appropriately."""
     fast = fast_agent
 
     # Define the agent
     @fast.agent(
         "agent",
-        instruction="You are a helpful AI Agent",
+        instruction="You are a helpful AI Agent. You have PDF support and summarisation capabilities.",
         servers=["image_server"],
         model=model_name,
     )
@@ -120,6 +156,7 @@ async def agent_function():
     [
         "gpt-4.1-mini",  # OpenAI model
         "haiku35",  # Anthropic model
+        "gemini25",  # This currently uses the OpenAI format. Google Gemini cannot process PDFs with the OpenAI format. It can only do so with the native Gemini format.
     ],
 )
 async def test_agent_with_pdf_prompt(fast_agent, model_name):
@@ -158,13 +195,12 @@ async def agent_function():
     ],
 )
 async def test_agent_includes_tool_results_in_multipart_result_anthropic(fast_agent, model_name):
-    """Test that the agent can process a PDF document and respond appropriately."""
     fast = fast_agent
 
     # Define the agent
     @fast.agent(
         "agent",
-        instruction="You are a helpful AI Agent",
+        instruction="You are a helpful AI Agent. You have vision capabilities and can analyse the image.",
         servers=["image_server"],
         model=model_name,
     )
@@ -173,7 +209,7 @@ async def agent_function():
             response: PromptMessageMultipart = await agent.agent.generate(
                 [
                     Prompt.user(
-                        "Use the image fetch tool to get the sample image and tell me what user name contained in this image?"
+                        "Use the image fetch tool to get the sample image and tell me the user name contained in this image?"
                     )
                 ]
             )
@@ -202,7 +238,7 @@ async def test_agent_includes_tool_results_in_multipart_result_openai(fast_agent
     # Define the agent
     @fast.agent(
         "agent",
-        instruction="You are a helpful AI Agent",
+        instruction="You are a helpful AI Agent. You have vision capabilities.",
         servers=["image_server"],
         model=model_name,
     )
@@ -211,16 +247,48 @@ async def agent_function():
             response: PromptMessageMultipart = await agent.agent.generate(
                 [
                     Prompt.user(
-                        "Use the image fetch tool to get the sample image and tell me what user name contained in this image?"
+                        "Use the image fetch tool to get the sample image and tell me the user name contained in this image?"
                     )
                 ]
             )
-            # OpenAI returns None for the first function call - different from Anthropic
-            # we are expecting a  tool call, tool response (1* text, 1 * image), final response
-            assert 3 == len(response.content)
-            assert "evalstate" in response.all_text()
-            # make sure it's available in the history
-            assert 3 == len(agent.agent._llm.message_history[1].content)
+            # Import TextContent for type checking
+            from mcp_agent.mcp_types import TextContent
+
+            def is_thought_part(part_content):
+                # Check if it's a TextContent and if its text starts with "thought" (case-insensitive)
+                return isinstance(
+                    part_content, TextContent
+                ) and part_content.text.strip().lower().startswith("thought")
+
+            # Filter out thought parts from the response content
+            filtered_response_content = [
+                part for part in response.content if not is_thought_part(part)
+            ]
+
+            # Filter out thought parts from the history message content
+            # Assuming message_history[1] is the relevant assistant message after the tool call
+            filtered_history_content = []
+            if (
+                len(agent.agent._llm.message_history) > 1
+                and agent.agent._llm.message_history[1].content
+            ):
+                filtered_history_content = [
+                    part
+                    for part in agent.agent._llm.message_history[1].content
+                    if not is_thought_part(part)
+                ]
+
+            # After filtering thoughts, we expect 3 semantic parts in the response:
+            # 1. TextContent introduction for the image (from tool result)
+            # 2. ImageContent (from tool result)
+            # 3. TextContent with the final LLM answer
+            assert 3 == len(filtered_response_content)
+            assert (
+                "evalstate" in response.all_text()
+            )  # response.all_text() will include thoughts, which is fine for this check.
+
+            # Ensure the filtered history also reflects the 3 semantic parts
+            assert 3 == len(filtered_history_content)
 
     # Execute the agent function
     await agent_function()
diff --git a/tests/e2e/prompts-resources/fastagent.jsonl b/tests/e2e/prompts-resources/fastagent.jsonl
index 13ce04101..e23d372e3 100644
--- a/tests/e2e/prompts-resources/fastagent.jsonl
+++ b/tests/e2e/prompts-resources/fastagent.jsonl
@@ -5,3 +5,15 @@
 {"level":"ERROR","timestamp":"2025-03-29T22:49:28.731609","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
 {"level":"ERROR","timestamp":"2025-03-29T22:49:29.257225","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
 {"level":"ERROR","timestamp":"2025-03-29T22:49:29.802053","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:52.593285","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:53.296582","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:53.996681","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:54.737177","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:55.430480","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:56.162954","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:56.906947","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:57.626598","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:58.335264","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:59.108578","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:38:59.829204","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"prompt_server: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"prompt_server"}}}
+{"level":"ERROR","timestamp":"2025-05-26T10:54:32.222518","namespace":"mcp_agent.llm.providers.augmented_llm_openai.agent","message":"Error: Error code: 400 - [{'error': {'code': 400, 'message': 'Invalid content part type: file', 'status': 'INVALID_ARGUMENT'}}]"}
diff --git a/tests/e2e/prompts-resources/test_prompts.py b/tests/e2e/prompts-resources/test_prompts.py
index 0c040b2a0..4e4e2ab87 100644
--- a/tests/e2e/prompts-resources/test_prompts.py
+++ b/tests/e2e/prompts-resources/test_prompts.py
@@ -10,6 +10,7 @@
     [
         "gpt-4.1-mini",  # OpenAI model
         "haiku35",  # Anthropic model
+        "gemini25",  # Google Gemini model -> Works. DONE.
     ],
 )
 async def test_agent_with_simple_prompt(fast_agent, model_name):
@@ -40,6 +41,7 @@ async def agent_function():
     [
         "gpt-4.1-mini",  # OpenAI model
         "haiku35",  # Anthropic model
+        # "gemini25",  # Google Gemini model -> This involves opening a PDF. It is not supported by Google Gemini with the OpenAI format. Unless the format is changed to the native Gemini format, this will not work.
     ],
 )
 async def test_agent_with_prompt_attachment(fast_agent, model_name):
@@ -70,6 +72,7 @@ async def agent_function():
     [
         "gpt-4.1-mini",  # OpenAI model
         "haiku35",  # Anthropic model
+        "gemini25",  # Google Gemini model -> Works. DONE.
     ],
 )
 async def test_agent_multiturn_prompt(fast_agent, model_name):
diff --git a/tests/e2e/sampling/fastagent.jsonl b/tests/e2e/sampling/fastagent.jsonl
index a100ecfed..ec99b47d6 100644
--- a/tests/e2e/sampling/fastagent.jsonl
+++ b/tests/e2e/sampling/fastagent.jsonl
@@ -1,2 +1,5 @@
 {"level":"ERROR","timestamp":"2025-03-29T22:49:37.743115","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"sampling_resource_anthropic: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"sampling_resource_anthropic"}}}
 {"level":"ERROR","timestamp":"2025-03-29T22:49:38.162289","namespace":"mcp_agent.mcp.mcp_connection_manager","message":"sampling_resource_openai: Lifecycle task encountered an error: generator didn't stop after athrow()","data":{"exc_info":true,"data":{"progress_action":"Error","server_name":"sampling_resource_openai"}}}
+{"level":"ERROR","timestamp":"2025-05-25T21:39:00.063320","namespace":"mcp_agent.mcp.sampling","message":"Error in sampling: Anthropic API key not configured\n\nThe Anthropic API key is required but not set.\nAdd it to your configuration file under anthropic.api_key or set the ANTHROPIC_API_KEY environment variable."}
+{"level":"ERROR","timestamp":"2025-05-25T21:39:00.831547","namespace":"mcp_agent.mcp.sampling","message":"Error in sampling: Openai API key not configured\n\nThe Openai API key is required but not set.\nAdd it to your configuration file under openai.api_key or set the OPENAI_API_KEY environment variable."}
+{"level":"ERROR","timestamp":"2025-05-25T21:39:01.599545","namespace":"mcp_agent.mcp.sampling","message":"Error in sampling: Anthropic API key not configured\n\nThe Anthropic API key is required but not set.\nAdd it to your configuration file under anthropic.api_key or set the ANTHROPIC_API_KEY environment variable."}
diff --git a/tests/e2e/smoke/base/test_e2e_smoke.py b/tests/e2e/smoke/base/test_e2e_smoke.py
index f0a616c8b..b8cdd8140 100644
--- a/tests/e2e/smoke/base/test_e2e_smoke.py
+++ b/tests/e2e/smoke/base/test_e2e_smoke.py
@@ -26,7 +26,10 @@
         #        "generic.qwen2.5:latest",
         #        "generic.llama3.2:latest",
         "openrouter.google/gemini-2.0-flash-001",
+        "googleoai.gemini-2.5-flash-preview-05-20",
         "google.gemini-2.0-flash",
+        "gemini2",
+        "gemini25",  # Works -> Done. Works most of the time, unless Gemini decides to write very long outputs.
         "azure.gpt-4.1",
     ],
 )
@@ -94,11 +97,12 @@ async def agent_function():
         "haiku35",  # Anthropic model
         "deepseek",
         "openrouter.google/gemini-2.0-flash-001",
+        "gemini2",
+        "gemini25",  # Works -> DONE.
         "o3-mini.low",
     ],
 )
 async def test_multiple_text_blocks_prompting(fast_agent, model_name):
-    """Test that the agent can process an image and respond appropriately."""
     fast = fast_agent
 
     # Define the agent
@@ -181,6 +185,8 @@ class WeatherForecast(BaseModel):
         "o3-mini.low",  # reasoning
         "gpt-4.1-nano",
         "gpt-4.1-mini",
+        "gemini2",
+        "gemini25",  # Works -> DONE.
         "azure.gpt-4.1",
     ],
 )
@@ -294,8 +300,9 @@ async def agent_function():
         "gpt-4o",
         "gpt-4.1-nano",
         "gpt-4.1-mini",
-        "google.gemini-2.0-flash",
+        "gemini2",
         "openrouter.google/gemini-2.0-flash-001",
+        "gemini25",
         "o3-mini.low",
         "azure.gpt-4.1",
     ],
@@ -306,7 +313,7 @@ async def test_basic_tool_calling(fast_agent, model_name):
 
     @fast.agent(
         "weatherforecast",
-        instruction="You are a helpful assistant that provides syntehsized weather data for testing"
+        instruction="You are a helpful assistant that provides synthesized weather data for testing"
         " purposes.",
         model=model_name,
         servers=["test_server"],
@@ -340,7 +347,8 @@ async def weather_forecast():
         "haiku35",
         "gpt-4o",
         "gpt-4.1-mini",
-        "google.gemini-2.0-flash",
+        "gemini2",
+        "gemini25",  # Works -> DONE.
         "openrouter.anthropic/claude-3.7-sonnet",
         "azure.gpt-4.1",
     ],
@@ -375,6 +383,7 @@ async def tools_no_args():
         #     "gpt-4.1-nano",
         "gpt-4.1-mini",
         "google.gemini-2.0-flash",
+        "gemini25",  # Works -> DONE.
         #       "openrouter.anthropic/claude-3.7-sonnet",
     ],
 )
@@ -409,6 +418,7 @@ async def test_tool_calls_no_args_typescript(fast_agent, model_name):
         "haiku35",
         "gpt-4.1",
         "google.gemini-2.0-flash",
+        "gemini25",  # Works -> DONE.
     ],
 )
 async def test_server_has_hyphen(fast_agent, model_name):
diff --git a/tests/e2e/structured/test_structured_outputs.py b/tests/e2e/structured/test_structured_outputs.py
index 11c0b9bd4..d768f1eec 100644
--- a/tests/e2e/structured/test_structured_outputs.py
+++ b/tests/e2e/structured/test_structured_outputs.py
@@ -30,6 +30,7 @@ class FormattedResponse(BaseModel):
         "gpt-4.1-mini",
         "o3-mini.low",
         "openrouter.google/gemini-2.0-flash-001",
+        "gemini25",
     ],
 )
 async def test_structured_output_with_automatic_format_for_model(fast_agent, model_name):
@@ -62,6 +63,7 @@ async def create_structured():
     "model_name",
     [
         "gpt-4.1-mini",
+        "gemini25",
     ],
 )
 async def test_structured_output_parses_assistant_message_if_last(fast_agent, model_name):
@@ -125,7 +127,8 @@ async def create_structured():
         # "haiku", -- anthropic do not support structured outputs this way
         "gpt-4.1-mini",
         "openrouter.google/gemini-2.0-flash-001",
-        "google.gemini-2.0-flash",
+        "gemini2",
+        "gemini25",
     ],
 )
 async def test_structured_output_with_response_format_overriden(fast_agent, model_name):
@@ -157,7 +160,12 @@ async def create_structured():
 @pytest.mark.e2e
 @pytest.mark.parametrize(
     "model_name",
-    ["gpt-4.1-mini", "haiku", "google.gemini-2.0-flash"],
+    [
+        "gpt-4.1-mini", 
+        "haiku", 
+        "gemini2",
+        "gemini25",
+    ],
 )
 async def test_history_management_with_structured(fast_agent, model_name):
     """Test that the agent can generate structured response with response_format_specified."""
diff --git a/tests/e2e/workflow/test_router_agent_e2e.py b/tests/e2e/workflow/test_router_agent_e2e.py
index 0ea1b0d5a..a8197aa4c 100644
--- a/tests/e2e/workflow/test_router_agent_e2e.py
+++ b/tests/e2e/workflow/test_router_agent_e2e.py
@@ -9,7 +9,11 @@
 @pytest.mark.integration
 @pytest.mark.asyncio
 @pytest.mark.e2e
-@pytest.mark.parametrize("model_name", ["haiku", "gpt-4.1-mini"])
+@pytest.mark.parametrize("model_name", [
+    "haiku", 
+    "gpt-4.1-mini",
+    "gemini25",
+])
 async def test_basic_text_routing(fast_agent, model_name):
     """Test that the agent can process an image and respond appropriately."""
     fast = fast_agent
diff --git a/tests/unit/mcp_agent/llm/providers/test_multipart_converter_google.py b/tests/unit/mcp_agent/llm/providers/test_multipart_converter_google.py
new file mode 100644
index 000000000..0a65e9307
--- /dev/null
+++ b/tests/unit/mcp_agent/llm/providers/test_multipart_converter_google.py
@@ -0,0 +1,85 @@
+import base64
+import unittest
+from typing import TYPE_CHECKING, List
+
+from mcp.types import (
+    CallToolResult,
+    ImageContent,
+    TextContent,
+)
+
+from mcp_agent.llm.providers.google_converter import GoogleConverter
+
+if TYPE_CHECKING:
+    from google.genai.types import Content
+
+
+class TestOpenAIToolConverter(unittest.TestCase):
+    """Test cases for conversion of tool results to OpenAI tool messages."""
+
+    def setUp(self):
+        """Set up test data."""
+        self.sample_text = "This is a tool result"
+        self.converter = GoogleConverter()
+
+    def test_tool_result_conversion(self):
+        """Test conversion of CallToolResult to OpenAI tool message."""
+        # Create a tool result with text content
+        text_content = TextContent(type="text", text=self.sample_text)
+        tool_result = CallToolResult(content=[text_content], isError=False)
+
+        # Create a tool call ID
+        #        tool_call_id = "call_abc123"
+
+        # Convert directly to OpenAI tool message
+        converted: List[Content] = self.converter.convert_function_results_to_google(
+            [("test", tool_result)]
+        )
+        assert 1 == len(converted)
+        assert "tool" == converted[0].role
+        assert self.sample_text == converted[0].parts[0].function_response.response["text_content"]
+
+    def test_multiple_tool_results_with_mixed_content(self):
+        """Test conversion of multiple tool results with different content types."""
+        # Create first tool result with text only
+        text_result = CallToolResult(
+            content=[TextContent(type="text", text="Text-only result")], isError=False
+        )
+
+        # Create second tool result with image
+        image_base64 = base64.b64encode(b"fake_image_data").decode("utf-8")
+        image_content = ImageContent(type="image", data=image_base64, mimeType="image/jpeg")
+        image_result = CallToolResult(
+            content=[TextContent(type="text", text="Here's the image:"), image_content],
+            isError=False,
+        )
+
+        # Create tool call IDs
+        tool_call_id1 = "call_text_only"
+        tool_call_id2 = "call_with_image"
+
+        # Create a list of (tool_call_id, result) tuples
+        results = [(tool_call_id1, text_result), (tool_call_id2, image_result)]
+
+        # Convert to OpenAI tool messages
+        converted: List[Content] = self.converter.convert_function_results_to_google(results)
+
+        # Assertions
+        assert 2 == len(converted)
+        assert 1 == len(converted[0].parts)  # Text Only
+        assert 2 == len(converted[1].parts[0].function_response.response)  # Text and Image
+
+
+#        assert self.sample_text == converted[0].parts[0].function_response.response["text"][0]
+
+# # Check first tool message (text only)
+# self.assertEqual(tool_messages[0]["role"], "tool")
+# self.assertEqual(tool_messages[0]["tool_call_id"], tool_call_id1)
+# self.assertEqual(tool_messages[0]["content"], "Text-only result")
+
+# # Check second tool message (with image)
+# self.assertEqual(tool_messages[1]["role"], "tool")
+# self.assertEqual(tool_messages[1]["tool_call_id"], tool_call_id2)
+# self.assertEqual(tool_messages[1]["content"], "Here's the image:")
+# self.assertEqual(tool_messages[2]["role"], "user")
+# self.assertEqual(tool_messages[2]["content"][0]["type"], "image_url")
diff --git a/uv.lock b/uv.lock
index 67d20f93b..392a987e6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.11'",
@@ -13,18 +13,18 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pydantic" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/80/1e/2fa0c5f12fc2e91e73fbe005b3cfbf80d3eb5a36c840cdf7fd7841baae79/a2a_types-0.1.0.tar.gz", hash = "sha256:f94097ed4425d9aacd55c508d77897937e14f56a89c974024abf9f31b280a355", size = 12070 }
+sdist = { url = "https://files.pythonhosted.org/packages/80/1e/2fa0c5f12fc2e91e73fbe005b3cfbf80d3eb5a36c840cdf7fd7841baae79/a2a_types-0.1.0.tar.gz", hash = "sha256:f94097ed4425d9aacd55c508d77897937e14f56a89c974024abf9f31b280a355", size = 12070, upload-time = "2025-04-13T11:21:27.53Z" }
 wheels = [
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 [[package]]
@@ -1047,9 +1092,9 @@ source = { registry = "https://pypi.org/simple" }
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