diff --git a/docs-website/reference/integrations-api/amazon_bedrock.md b/docs-website/reference/integrations-api/amazon_bedrock.md
index 5bb9645dc5..a9383e8173 100644
--- a/docs-website/reference/integrations-api/amazon_bedrock.md
+++ b/docs-website/reference/integrations-api/amazon_bedrock.md
@@ -1135,7 +1135,7 @@ def __init__(
         generation_kwargs: Optional[Dict[str, Any]] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
         boto3_config: Optional[Dict[str, Any]] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None,
+        tools: Optional[ToolsType] = None,
         *,
         guardrail_config: Optional[Dict[str, str]] = None) -> None
 ```
@@ -1168,7 +1168,8 @@ function that handles the streaming chunks. The callback function receives a
 [StreamingChunk](https://docs.haystack.deepset.ai/docs/data-classes#streamingchunk) object and switches
 the streaming mode on.
 - `boto3_config`: The configuration for the boto3 client.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `guardrail_config`: Optional configuration for a guardrail that has been created in Amazon Bedrock.
 This must be provided as a dictionary matching either
 [GuardrailConfiguration](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GuardrailConfiguration.html).
@@ -1227,12 +1228,10 @@ Instance of `AmazonBedrockChatGenerator`.
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-def run(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+def run(messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Executes a synchronous inference call to the Amazon Bedrock model using the Converse API.
@@ -1248,7 +1247,8 @@ Supports both standard and streaming responses depending on whether a streaming
 - `stopSequences`: List of stop sequences to stop generation.
 - `temperature`: Sampling temperature.
 - `topP`: Nucleus sampling parameter.
-- `tools`: Optional list of Tools that the model may call during execution.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 **Raises**:
 
@@ -1265,11 +1265,10 @@ A dictionary containing the model-generated replies under the `"replies"` key.
 ```python
 @component.output_types(replies=List[ChatMessage])
 async def run_async(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Executes an asynchronous inference call to the Amazon Bedrock model using the Converse API.
@@ -1285,7 +1284,8 @@ Designed for use cases where non-blocking or concurrent execution is desired.
 - `stopSequences`: List of stop sequences to stop generation.
 - `temperature`: Sampling temperature.
 - `topP`: Nucleus sampling parameter.
-- `tools`: Optional list of Tool objects or a Toolset that the model can use.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 **Raises**:
 
diff --git a/docs-website/reference/integrations-api/anthropic.md b/docs-website/reference/integrations-api/anthropic.md
index 4fadc67306..0ea96cf58f 100644
--- a/docs-website/reference/integrations-api/anthropic.md
+++ b/docs-website/reference/integrations-api/anthropic.md
@@ -196,7 +196,7 @@ def __init__(api_key: Secret = Secret.from_env_var("ANTHROPIC_API_KEY"),
              streaming_callback: Optional[StreamingCallbackT] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              ignore_tools_thinking_messages: bool = True,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              *,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None)
@@ -231,7 +231,8 @@ Supported generation_kwargs parameters are:
 `ignore_tools_thinking_messages` is `True`, the generator will drop so-called thinking messages when tool
 use is detected. See the Anthropic [tools](https://docs.anthropic.com/en/docs/tool-use#chain-of-thought-tool-use)
 for more details.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name.
 - `timeout`: Timeout for Anthropic client calls. If not set, it defaults to the default set by the Anthropic client.
 - `max_retries`: Maximum number of retries to attempt for failed requests. If not set, it defaults to the default set by
 the Anthropic client.
@@ -275,12 +276,10 @@ The deserialized component instance.
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-def run(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+def run(messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Invokes the Anthropic API with the given messages and generation kwargs.
@@ -290,8 +289,9 @@ Invokes the Anthropic API with the given messages and generation kwargs.
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 - `generation_kwargs`: Optional arguments to pass to the Anthropic generation endpoint.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should
-have a unique name. If set, it will override the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name. If set, it will override the `tools` parameter set during component
+initialization.
 
 **Returns**:
 
@@ -305,11 +305,10 @@ A dictionary with the following keys:
 ```python
 @component.output_types(replies=List[ChatMessage])
 async def run_async(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Async version of the run method. Invokes the Anthropic API with the given messages and generation kwargs.
@@ -319,8 +318,9 @@ Async version of the run method. Invokes the Anthropic API with the given messag
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 - `generation_kwargs`: Optional arguments to pass to the Anthropic generation endpoint.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should
-have a unique name. If set, it will override the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name. If set, it will override the `tools` parameter set during component
+initialization.
 
 **Returns**:
 
@@ -392,7 +392,7 @@ def __init__(region: str,
                                                    None]] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              ignore_tools_thinking_messages: bool = True,
-             tools: Optional[List[Tool]] = None,
+             tools: Optional[ToolsType] = None,
              *,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None)
@@ -425,7 +425,8 @@ Supported generation_kwargs parameters are:
 `ignore_tools_thinking_messages` is `True`, the generator will drop so-called thinking messages when tool
 use is detected. See the Anthropic [tools](https://docs.anthropic.com/en/docs/tool-use#chain-of-thought-tool-use)
 for more details.
-- `tools`: A list of Tool objects that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name.
 - `timeout`: Timeout for Anthropic client calls. If not set, it defaults to the default set by the Anthropic client.
 - `max_retries`: Maximum number of retries to attempt for failed requests. If not set, it defaults to the default set by
 the Anthropic client.
diff --git a/docs-website/reference/integrations-api/cohere.md b/docs-website/reference/integrations-api/cohere.md
index 8e1c14fb52..5062839bef 100644
--- a/docs-website/reference/integrations-api/cohere.md
+++ b/docs-website/reference/integrations-api/cohere.md
@@ -745,7 +745,7 @@ def __init__(api_key: Secret = Secret.from_env_var(
              streaming_callback: Optional[StreamingCallbackT] = None,
              api_base_url: Optional[str] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              **kwargs: Any)
 ```
 
@@ -770,7 +770,8 @@ Some of the parameters are:
   `accurate` results or `fast` results.
 - 'temperature': A non-negative float that tunes the degree of randomness in generation. Lower temperatures
   mean less random generations.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset that the model can use.
+Each tool should have a unique name.
 
 <a id="haystack_integrations.components.generators.cohere.chat.chat_generator.CohereChatGenerator.to_dict"></a>
 
@@ -814,7 +815,7 @@ Deserialized component.
 def run(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
 ```
@@ -828,8 +829,8 @@ Invoke the chat endpoint based on the provided messages and generation parameter
 potentially override the parameters passed in the __init__ method.
 For more details on the parameters supported by the Cohere API, refer to the
 Cohere [documentation](https://docs.cohere.com/reference/chat).
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. If set, it will override
-the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 The callback function accepts StreamingChunk as an argument.
 
@@ -847,7 +848,7 @@ A dictionary with the following keys:
 async def run_async(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
 ```
@@ -861,8 +862,8 @@ Asynchronously invoke the chat endpoint based on the provided messages and gener
 potentially override the parameters passed in the __init__ method.
 For more details on the parameters supported by the Cohere API, refer to the
 Cohere [documentation](https://docs.cohere.com/reference/chat).
-- `tools`: A list of tools for which the model can prepare calls. If set, it will override
-the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 
 **Returns**:
diff --git a/docs-website/reference/integrations-api/google_genai.md b/docs-website/reference/integrations-api/google_genai.md
index bd51dae768..e9cc647592 100644
--- a/docs-website/reference/integrations-api/google_genai.md
+++ b/docs-website/reference/integrations-api/google_genai.md
@@ -131,7 +131,7 @@ def __init__(*,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              safety_settings: Optional[List[Dict[str, Any]]] = None,
              streaming_callback: Optional[StreamingCallbackT] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None)
+             tools: Optional[ToolsType] = None)
 ```
 
 Initialize a GoogleGenAIChatGenerator instance.
@@ -156,7 +156,8 @@ For Gemini 2.5 series, supports `thinking_budget` to configure thinking behavior
   - Positive integer: Set explicit budget
 - `safety_settings`: Safety settings for content filtering
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 <a id="haystack_integrations.components.generators.google_genai.chat.chat_generator.GoogleGenAIChatGenerator.to_dict"></a>
 
@@ -201,7 +202,7 @@ def run(messages: List[ChatMessage],
         generation_kwargs: Optional[Dict[str, Any]] = None,
         safety_settings: Optional[List[Dict[str, Any]]] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None) -> Dict[str, Any]
+        tools: Optional[ToolsType] = None) -> Dict[str, Any]
 ```
 
 Run the Google Gen AI chat generator on the given input data.
@@ -215,8 +216,8 @@ the default config. Supports `thinking_budget` for Gemini 2.5 series thinking co
 default settings.
 - `streaming_callback`: A callback function that is called when a new token is
 received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. If provided, it will
-override the tools set during initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If provided, it will override the tools set during initialization.
 
 **Raises**:
 
@@ -235,12 +236,11 @@ A dictionary with the following keys:
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-async def run_async(
-        messages: List[ChatMessage],
-        generation_kwargs: Optional[Dict[str, Any]] = None,
-        safety_settings: Optional[List[Dict[str, Any]]] = None,
-        streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None) -> Dict[str, Any]
+async def run_async(messages: List[ChatMessage],
+                    generation_kwargs: Optional[Dict[str, Any]] = None,
+                    safety_settings: Optional[List[Dict[str, Any]]] = None,
+                    streaming_callback: Optional[StreamingCallbackT] = None,
+                    tools: Optional[ToolsType] = None) -> Dict[str, Any]
 ```
 
 Async version of the run method. Run the Google Gen AI chat generator on the given input data.
@@ -255,8 +255,8 @@ See https://ai.google.dev/gemini-api/docs/thinking for possible values.
 default settings.
 - `streaming_callback`: A callback function that is called when a new token is
 received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. If provided, it will
-override the tools set during initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If provided, it will override the tools set during initialization.
 
 **Raises**:
 
diff --git a/docs-website/reference/integrations-api/mcp.md b/docs-website/reference/integrations-api/mcp.md
index fb75acbede..73471fc22c 100644
--- a/docs-website/reference/integrations-api/mcp.md
+++ b/docs-website/reference/integrations-api/mcp.md
@@ -673,7 +673,8 @@ def __init__(name: str,
              server_info: MCPServerInfo,
              description: str | None = None,
              connection_timeout: int = 30,
-             invocation_timeout: int = 30)
+             invocation_timeout: int = 30,
+             eager_connect: bool = False)
 ```
 
 Initialize the MCP tool.
@@ -685,6 +686,9 @@ Initialize the MCP tool.
 - `description`: Custom description (if None, server description will be used)
 - `connection_timeout`: Timeout in seconds for server connection
 - `invocation_timeout`: Default timeout in seconds for tool invocations
+- `eager_connect`: If True, connect to server during initialization.
+If False (default), defer connection until warm_up or first tool use,
+whichever comes first.
 
 **Raises**:
 
@@ -715,6 +719,16 @@ Asynchronous tool invocation.
 
 JSON string representation of the tool invocation result
 
+<a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.warm_up"></a>
+
+#### MCPTool.warm\_up
+
+```python
+def warm_up() -> None
+```
+
+Connect and fetch the tool schema if eager_connect is turned off.
+
 <a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.to_dict"></a>
 
 #### MCPTool.to\_dict
@@ -792,20 +806,6 @@ def tool_spec() -> dict[str, Any]
 
 Return the Tool specification to be used by the Language Model.
 
-<a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.warm_up"></a>
-
-#### MCPTool.warm\_up
-
-```python
-def warm_up() -> None
-```
-
-Prepare the Tool for use.
-
-Override this method to establish connections to remote services, load models,
-or perform other resource-intensive initialization. This method should be idempotent,
-as it may be called multiple times.
-
 <a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.invoke"></a>
 
 #### MCPTool.invoke
@@ -953,7 +953,8 @@ sse_toolset = MCPToolset(
 def __init__(server_info: MCPServerInfo,
              tool_names: list[str] | None = None,
              connection_timeout: float = 30.0,
-             invocation_timeout: float = 30.0)
+             invocation_timeout: float = 30.0,
+             eager_connect: bool = False)
 ```
 
 Initialize the MCP toolset.
@@ -965,11 +966,27 @@ Initialize the MCP toolset.
 matching names will be added to the toolset.
 - `connection_timeout`: Timeout in seconds for server connection
 - `invocation_timeout`: Default timeout in seconds for tool invocations
+- `eager_connect`: If True, connect to server and load tools during initialization.
+If False (default), defer connection to warm_up.
 
 **Raises**:
 
 - `MCPToolNotFoundError`: If any of the specified tool names are not found on the server
 
+<a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.warm_up"></a>
+
+#### MCPToolset.warm\_up
+
+```python
+def warm_up() -> None
+```
+
+Connect and load tools when eager_connect is turned off.
+
+This method is automatically called by ``ToolInvoker.warm_up()`` and ``Pipeline.warm_up()``.
+You can also call it directly before using the toolset to ensure all tool schemas
+are available without performing a real invocation.
+
 <a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.to_dict"></a>
 
 #### MCPToolset.to\_dict
@@ -1063,19 +1080,6 @@ Supports checking by:
 
 True if contained, False otherwise
 
-<a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.warm_up"></a>
-
-#### MCPToolset.warm\_up
-
-```python
-def warm_up() -> None
-```
-
-Prepare the Toolset for use.
-
-Override this method to set up shared resources like database connections or HTTP sessions.
-This method should be idempotent, as it may be called multiple times.
-
 <a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.add"></a>
 
 #### MCPToolset.add
diff --git a/docs-website/reference/integrations-api/mistral.md b/docs-website/reference/integrations-api/mistral.md
index 432d8380f7..758ca5d2a8 100644
--- a/docs-website/reference/integrations-api/mistral.md
+++ b/docs-website/reference/integrations-api/mistral.md
@@ -382,8 +382,8 @@ Some of the supported parameters:
     Notes:
     - For structured outputs with streaming,
       the `response_format` must be a JSON schema and not a Pydantic model.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `timeout`: The timeout for the Mistral API call. If not set, it defaults to either the `OPENAI_TIMEOUT`
 environment variable, or 30 seconds.
 - `max_retries`: Maximum number of retries to contact OpenAI after an internal error.
diff --git a/docs-website/reference/integrations-api/ollama.md b/docs-website/reference/integrations-api/ollama.md
index 1ba5c76aca..eefffbd938 100644
--- a/docs-website/reference/integrations-api/ollama.md
+++ b/docs-website/reference/integrations-api/ollama.md
@@ -165,7 +165,7 @@ def __init__(model: str = "qwen3:0.6b",
              keep_alive: Optional[Union[float, str]] = None,
              streaming_callback: Optional[Callable[[StreamingChunk],
                                                    None]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              response_format: Optional[Union[None, Literal["json"],
                                              JsonSchemaValue]] = None,
              think: Union[bool, Literal["low", "medium", "high"]] = False)
@@ -200,9 +200,9 @@ The name of the model to use. The model must already be present (pulled) in the
     A callback function that is called when a new token is received from the stream.
     The callback function accepts StreamingChunk as an argument.
 :param tools:
-    A list of `haystack.tools.Tool` or a `haystack.tools.Toolset`. Duplicate tool names raise a `ValueError`.
-    Not all models support tools. For a list of models compatible with tools, see the
-    [models page](https://ollama.com/search?c=tools).
+    A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+    Each tool should have a unique name. Not all models support tools. For a list of models compatible
+    with tools, see the [models page](https://ollama.com/search?c=tools).
 :param response_format:
     The format for structured model outputs. The value can be:
     - None: No specific structure or format is applied to the response. The response is returned as-is.
@@ -253,7 +253,7 @@ Deserialized component.
 def run(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     *,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
@@ -268,9 +268,8 @@ Runs an Ollama Model on a given chat history.
 These are merged on top of the instance-level `generation_kwargs`.
 Optional arguments to pass to the Ollama generation endpoint, such as temperature, top_p, etc. See the
 [Ollama docs](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance. If set, it will override the `tools` parameter set
-during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callable to receive `StreamingChunk` objects as they
 arrive.  Supplying a callback (here or in the constructor) switches
 the component into streaming mode.
@@ -289,7 +288,7 @@ A dictionary with the following keys:
 async def run_async(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     *,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
@@ -302,7 +301,7 @@ Async version of run. Runs an Ollama Model on a given chat history.
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `generation_kwargs`: Per-call overrides for Ollama inference options.
 These are merged on top of the instance-level `generation_kwargs`.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
 If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callable to receive `StreamingChunk` objects as they arrive.
 Supplying a callback switches the component into streaming mode.
diff --git a/docs-website/reference/integrations-api/snowflake.md b/docs-website/reference/integrations-api/snowflake.md
index 064d8a9e5c..45e4f73551 100644
--- a/docs-website/reference/integrations-api/snowflake.md
+++ b/docs-website/reference/integrations-api/snowflake.md
@@ -32,6 +32,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### Key-pair Authentication (MFA):
@@ -46,6 +47,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### OAuth Authentication (MFA):
@@ -61,6 +63,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### Running queries:
@@ -93,17 +96,23 @@ shape: (3, 3)
 ```python
 def __init__(user: str,
              account: str,
-             authenticator: Literal["SNOWFLAKE", "SNOWFLAKE_JWT", "OAUTH"],
-             api_key: Optional[Secret] = None,
+             authenticator: Literal["SNOWFLAKE", "SNOWFLAKE_JWT",
+                                    "OAUTH"] = "SNOWFLAKE",
+             api_key: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_API_KEY", strict=False),
              database: Optional[str] = None,
              db_schema: Optional[str] = None,
              warehouse: Optional[str] = None,
              login_timeout: Optional[int] = 60,
              return_markdown: bool = True,
-             private_key_file: Optional[Secret] = None,
-             private_key_file_pwd: Optional[Secret] = None,
-             oauth_client_id: Optional[Secret] = None,
-             oauth_client_secret: Optional[Secret] = None,
+             private_key_file: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_PRIVATE_KEY_FILE", strict=False),
+             private_key_file_pwd: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_PRIVATE_KEY_PWD", strict=False),
+             oauth_client_id: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_OAUTH_CLIENT_ID", strict=False),
+             oauth_client_secret: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_OAUTH_CLIENT_SECRET", strict=False),
              oauth_token_request_url: Optional[str] = None,
              oauth_authorization_url: Optional[str] = None) -> None
 ```
@@ -131,19 +140,15 @@ Required for OAUTH authentication.
 - `oauth_token_request_url`: OAuth token request URL for Client Credentials flow.
 - `oauth_authorization_url`: OAuth authorization URL for Authorization Code flow.
 
-<a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.test_connection"></a>
+<a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.warm_up"></a>
 
-#### SnowflakeTableRetriever.test\_connection
+#### SnowflakeTableRetriever.warm\_up
 
 ```python
-def test_connection() -> bool
+def warm_up() -> None
 ```
 
-Tests the connection with the current authentication settings.
-
-**Returns**:
-
-True if connection is successful, False otherwise.
+Warm up the component by initializing the authenticator handler and testing the database connection.
 
 <a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.to_dict"></a>
 
diff --git a/docs-website/reference/integrations-api/together_ai.md b/docs-website/reference/integrations-api/together_ai.md
index 8610d1ce98..5282c17ea3 100644
--- a/docs-website/reference/integrations-api/together_ai.md
+++ b/docs-website/reference/integrations-api/together_ai.md
@@ -231,7 +231,7 @@ def __init__(*,
              streaming_callback: Optional[StreamingCallbackT] = None,
              api_base_url: Optional[str] = "https://api.together.xyz/v1",
              generation_kwargs: Optional[Dict[str, Any]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None,
              http_client_kwargs: Optional[Dict[str, Any]] = None)
@@ -263,8 +263,8 @@ Some of the supported parameters:
     events as they become available, with the stream terminated by a data: [DONE] message.
 - `safe_prompt`: Whether to inject a safety prompt before all conversations.
 - `random_seed`: The seed to use for random sampling.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `timeout`: The timeout for the Together AI API call.
 - `max_retries`: Maximum number of retries to contact Together AI after an internal error.
 If not set, it defaults to either the `OPENAI_MAX_RETRIES` environment variable, or set to 5.
diff --git a/docs-website/reference_versioned_docs/version-2.17/integrations-api/amazon_bedrock.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/amazon_bedrock.md
index 5bb9645dc5..a9383e8173 100644
--- a/docs-website/reference_versioned_docs/version-2.17/integrations-api/amazon_bedrock.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/amazon_bedrock.md
@@ -1135,7 +1135,7 @@ def __init__(
         generation_kwargs: Optional[Dict[str, Any]] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
         boto3_config: Optional[Dict[str, Any]] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None,
+        tools: Optional[ToolsType] = None,
         *,
         guardrail_config: Optional[Dict[str, str]] = None) -> None
 ```
@@ -1168,7 +1168,8 @@ function that handles the streaming chunks. The callback function receives a
 [StreamingChunk](https://docs.haystack.deepset.ai/docs/data-classes#streamingchunk) object and switches
 the streaming mode on.
 - `boto3_config`: The configuration for the boto3 client.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `guardrail_config`: Optional configuration for a guardrail that has been created in Amazon Bedrock.
 This must be provided as a dictionary matching either
 [GuardrailConfiguration](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GuardrailConfiguration.html).
@@ -1227,12 +1228,10 @@ Instance of `AmazonBedrockChatGenerator`.
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-def run(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+def run(messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Executes a synchronous inference call to the Amazon Bedrock model using the Converse API.
@@ -1248,7 +1247,8 @@ Supports both standard and streaming responses depending on whether a streaming
 - `stopSequences`: List of stop sequences to stop generation.
 - `temperature`: Sampling temperature.
 - `topP`: Nucleus sampling parameter.
-- `tools`: Optional list of Tools that the model may call during execution.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 **Raises**:
 
@@ -1265,11 +1265,10 @@ A dictionary containing the model-generated replies under the `"replies"` key.
 ```python
 @component.output_types(replies=List[ChatMessage])
 async def run_async(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Executes an asynchronous inference call to the Amazon Bedrock model using the Converse API.
@@ -1285,7 +1284,8 @@ Designed for use cases where non-blocking or concurrent execution is desired.
 - `stopSequences`: List of stop sequences to stop generation.
 - `temperature`: Sampling temperature.
 - `topP`: Nucleus sampling parameter.
-- `tools`: Optional list of Tool objects or a Toolset that the model can use.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 **Raises**:
 
diff --git a/docs-website/reference_versioned_docs/version-2.17/integrations-api/anthropic.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/anthropic.md
index 4fadc67306..0ea96cf58f 100644
--- a/docs-website/reference_versioned_docs/version-2.17/integrations-api/anthropic.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/anthropic.md
@@ -196,7 +196,7 @@ def __init__(api_key: Secret = Secret.from_env_var("ANTHROPIC_API_KEY"),
              streaming_callback: Optional[StreamingCallbackT] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              ignore_tools_thinking_messages: bool = True,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              *,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None)
@@ -231,7 +231,8 @@ Supported generation_kwargs parameters are:
 `ignore_tools_thinking_messages` is `True`, the generator will drop so-called thinking messages when tool
 use is detected. See the Anthropic [tools](https://docs.anthropic.com/en/docs/tool-use#chain-of-thought-tool-use)
 for more details.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name.
 - `timeout`: Timeout for Anthropic client calls. If not set, it defaults to the default set by the Anthropic client.
 - `max_retries`: Maximum number of retries to attempt for failed requests. If not set, it defaults to the default set by
 the Anthropic client.
@@ -275,12 +276,10 @@ The deserialized component instance.
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-def run(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+def run(messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Invokes the Anthropic API with the given messages and generation kwargs.
@@ -290,8 +289,9 @@ Invokes the Anthropic API with the given messages and generation kwargs.
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 - `generation_kwargs`: Optional arguments to pass to the Anthropic generation endpoint.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should
-have a unique name. If set, it will override the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name. If set, it will override the `tools` parameter set during component
+initialization.
 
 **Returns**:
 
@@ -305,11 +305,10 @@ A dictionary with the following keys:
 ```python
 @component.output_types(replies=List[ChatMessage])
 async def run_async(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Async version of the run method. Invokes the Anthropic API with the given messages and generation kwargs.
@@ -319,8 +318,9 @@ Async version of the run method. Invokes the Anthropic API with the given messag
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 - `generation_kwargs`: Optional arguments to pass to the Anthropic generation endpoint.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should
-have a unique name. If set, it will override the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name. If set, it will override the `tools` parameter set during component
+initialization.
 
 **Returns**:
 
@@ -392,7 +392,7 @@ def __init__(region: str,
                                                    None]] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              ignore_tools_thinking_messages: bool = True,
-             tools: Optional[List[Tool]] = None,
+             tools: Optional[ToolsType] = None,
              *,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None)
@@ -425,7 +425,8 @@ Supported generation_kwargs parameters are:
 `ignore_tools_thinking_messages` is `True`, the generator will drop so-called thinking messages when tool
 use is detected. See the Anthropic [tools](https://docs.anthropic.com/en/docs/tool-use#chain-of-thought-tool-use)
 for more details.
-- `tools`: A list of Tool objects that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name.
 - `timeout`: Timeout for Anthropic client calls. If not set, it defaults to the default set by the Anthropic client.
 - `max_retries`: Maximum number of retries to attempt for failed requests. If not set, it defaults to the default set by
 the Anthropic client.
diff --git a/docs-website/reference_versioned_docs/version-2.17/integrations-api/cohere.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/cohere.md
index 8e1c14fb52..5062839bef 100644
--- a/docs-website/reference_versioned_docs/version-2.17/integrations-api/cohere.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/cohere.md
@@ -745,7 +745,7 @@ def __init__(api_key: Secret = Secret.from_env_var(
              streaming_callback: Optional[StreamingCallbackT] = None,
              api_base_url: Optional[str] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              **kwargs: Any)
 ```
 
@@ -770,7 +770,8 @@ Some of the parameters are:
   `accurate` results or `fast` results.
 - 'temperature': A non-negative float that tunes the degree of randomness in generation. Lower temperatures
   mean less random generations.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset that the model can use.
+Each tool should have a unique name.
 
 <a id="haystack_integrations.components.generators.cohere.chat.chat_generator.CohereChatGenerator.to_dict"></a>
 
@@ -814,7 +815,7 @@ Deserialized component.
 def run(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
 ```
@@ -828,8 +829,8 @@ Invoke the chat endpoint based on the provided messages and generation parameter
 potentially override the parameters passed in the __init__ method.
 For more details on the parameters supported by the Cohere API, refer to the
 Cohere [documentation](https://docs.cohere.com/reference/chat).
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. If set, it will override
-the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 The callback function accepts StreamingChunk as an argument.
 
@@ -847,7 +848,7 @@ A dictionary with the following keys:
 async def run_async(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
 ```
@@ -861,8 +862,8 @@ Asynchronously invoke the chat endpoint based on the provided messages and gener
 potentially override the parameters passed in the __init__ method.
 For more details on the parameters supported by the Cohere API, refer to the
 Cohere [documentation](https://docs.cohere.com/reference/chat).
-- `tools`: A list of tools for which the model can prepare calls. If set, it will override
-the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 
 **Returns**:
diff --git a/docs-website/reference_versioned_docs/version-2.17/integrations-api/google_genai.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/google_genai.md
index bd51dae768..e9cc647592 100644
--- a/docs-website/reference_versioned_docs/version-2.17/integrations-api/google_genai.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/google_genai.md
@@ -131,7 +131,7 @@ def __init__(*,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              safety_settings: Optional[List[Dict[str, Any]]] = None,
              streaming_callback: Optional[StreamingCallbackT] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None)
+             tools: Optional[ToolsType] = None)
 ```
 
 Initialize a GoogleGenAIChatGenerator instance.
@@ -156,7 +156,8 @@ For Gemini 2.5 series, supports `thinking_budget` to configure thinking behavior
   - Positive integer: Set explicit budget
 - `safety_settings`: Safety settings for content filtering
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 <a id="haystack_integrations.components.generators.google_genai.chat.chat_generator.GoogleGenAIChatGenerator.to_dict"></a>
 
@@ -201,7 +202,7 @@ def run(messages: List[ChatMessage],
         generation_kwargs: Optional[Dict[str, Any]] = None,
         safety_settings: Optional[List[Dict[str, Any]]] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None) -> Dict[str, Any]
+        tools: Optional[ToolsType] = None) -> Dict[str, Any]
 ```
 
 Run the Google Gen AI chat generator on the given input data.
@@ -215,8 +216,8 @@ the default config. Supports `thinking_budget` for Gemini 2.5 series thinking co
 default settings.
 - `streaming_callback`: A callback function that is called when a new token is
 received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. If provided, it will
-override the tools set during initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If provided, it will override the tools set during initialization.
 
 **Raises**:
 
@@ -235,12 +236,11 @@ A dictionary with the following keys:
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-async def run_async(
-        messages: List[ChatMessage],
-        generation_kwargs: Optional[Dict[str, Any]] = None,
-        safety_settings: Optional[List[Dict[str, Any]]] = None,
-        streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None) -> Dict[str, Any]
+async def run_async(messages: List[ChatMessage],
+                    generation_kwargs: Optional[Dict[str, Any]] = None,
+                    safety_settings: Optional[List[Dict[str, Any]]] = None,
+                    streaming_callback: Optional[StreamingCallbackT] = None,
+                    tools: Optional[ToolsType] = None) -> Dict[str, Any]
 ```
 
 Async version of the run method. Run the Google Gen AI chat generator on the given input data.
@@ -255,8 +255,8 @@ See https://ai.google.dev/gemini-api/docs/thinking for possible values.
 default settings.
 - `streaming_callback`: A callback function that is called when a new token is
 received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. If provided, it will
-override the tools set during initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If provided, it will override the tools set during initialization.
 
 **Raises**:
 
diff --git a/docs-website/reference_versioned_docs/version-2.17/integrations-api/mcp.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/mcp.md
index fb75acbede..73471fc22c 100644
--- a/docs-website/reference_versioned_docs/version-2.17/integrations-api/mcp.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/mcp.md
@@ -673,7 +673,8 @@ def __init__(name: str,
              server_info: MCPServerInfo,
              description: str | None = None,
              connection_timeout: int = 30,
-             invocation_timeout: int = 30)
+             invocation_timeout: int = 30,
+             eager_connect: bool = False)
 ```
 
 Initialize the MCP tool.
@@ -685,6 +686,9 @@ Initialize the MCP tool.
 - `description`: Custom description (if None, server description will be used)
 - `connection_timeout`: Timeout in seconds for server connection
 - `invocation_timeout`: Default timeout in seconds for tool invocations
+- `eager_connect`: If True, connect to server during initialization.
+If False (default), defer connection until warm_up or first tool use,
+whichever comes first.
 
 **Raises**:
 
@@ -715,6 +719,16 @@ Asynchronous tool invocation.
 
 JSON string representation of the tool invocation result
 
+<a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.warm_up"></a>
+
+#### MCPTool.warm\_up
+
+```python
+def warm_up() -> None
+```
+
+Connect and fetch the tool schema if eager_connect is turned off.
+
 <a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.to_dict"></a>
 
 #### MCPTool.to\_dict
@@ -792,20 +806,6 @@ def tool_spec() -> dict[str, Any]
 
 Return the Tool specification to be used by the Language Model.
 
-<a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.warm_up"></a>
-
-#### MCPTool.warm\_up
-
-```python
-def warm_up() -> None
-```
-
-Prepare the Tool for use.
-
-Override this method to establish connections to remote services, load models,
-or perform other resource-intensive initialization. This method should be idempotent,
-as it may be called multiple times.
-
 <a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.invoke"></a>
 
 #### MCPTool.invoke
@@ -953,7 +953,8 @@ sse_toolset = MCPToolset(
 def __init__(server_info: MCPServerInfo,
              tool_names: list[str] | None = None,
              connection_timeout: float = 30.0,
-             invocation_timeout: float = 30.0)
+             invocation_timeout: float = 30.0,
+             eager_connect: bool = False)
 ```
 
 Initialize the MCP toolset.
@@ -965,11 +966,27 @@ Initialize the MCP toolset.
 matching names will be added to the toolset.
 - `connection_timeout`: Timeout in seconds for server connection
 - `invocation_timeout`: Default timeout in seconds for tool invocations
+- `eager_connect`: If True, connect to server and load tools during initialization.
+If False (default), defer connection to warm_up.
 
 **Raises**:
 
 - `MCPToolNotFoundError`: If any of the specified tool names are not found on the server
 
+<a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.warm_up"></a>
+
+#### MCPToolset.warm\_up
+
+```python
+def warm_up() -> None
+```
+
+Connect and load tools when eager_connect is turned off.
+
+This method is automatically called by ``ToolInvoker.warm_up()`` and ``Pipeline.warm_up()``.
+You can also call it directly before using the toolset to ensure all tool schemas
+are available without performing a real invocation.
+
 <a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.to_dict"></a>
 
 #### MCPToolset.to\_dict
@@ -1063,19 +1080,6 @@ Supports checking by:
 
 True if contained, False otherwise
 
-<a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.warm_up"></a>
-
-#### MCPToolset.warm\_up
-
-```python
-def warm_up() -> None
-```
-
-Prepare the Toolset for use.
-
-Override this method to set up shared resources like database connections or HTTP sessions.
-This method should be idempotent, as it may be called multiple times.
-
 <a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.add"></a>
 
 #### MCPToolset.add
diff --git a/docs-website/reference_versioned_docs/version-2.17/integrations-api/mistral.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/mistral.md
index 432d8380f7..758ca5d2a8 100644
--- a/docs-website/reference_versioned_docs/version-2.17/integrations-api/mistral.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/mistral.md
@@ -382,8 +382,8 @@ Some of the supported parameters:
     Notes:
     - For structured outputs with streaming,
       the `response_format` must be a JSON schema and not a Pydantic model.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `timeout`: The timeout for the Mistral API call. If not set, it defaults to either the `OPENAI_TIMEOUT`
 environment variable, or 30 seconds.
 - `max_retries`: Maximum number of retries to contact OpenAI after an internal error.
diff --git a/docs-website/reference_versioned_docs/version-2.17/integrations-api/ollama.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/ollama.md
index 1ba5c76aca..eefffbd938 100644
--- a/docs-website/reference_versioned_docs/version-2.17/integrations-api/ollama.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/ollama.md
@@ -165,7 +165,7 @@ def __init__(model: str = "qwen3:0.6b",
              keep_alive: Optional[Union[float, str]] = None,
              streaming_callback: Optional[Callable[[StreamingChunk],
                                                    None]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              response_format: Optional[Union[None, Literal["json"],
                                              JsonSchemaValue]] = None,
              think: Union[bool, Literal["low", "medium", "high"]] = False)
@@ -200,9 +200,9 @@ The name of the model to use. The model must already be present (pulled) in the
     A callback function that is called when a new token is received from the stream.
     The callback function accepts StreamingChunk as an argument.
 :param tools:
-    A list of `haystack.tools.Tool` or a `haystack.tools.Toolset`. Duplicate tool names raise a `ValueError`.
-    Not all models support tools. For a list of models compatible with tools, see the
-    [models page](https://ollama.com/search?c=tools).
+    A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+    Each tool should have a unique name. Not all models support tools. For a list of models compatible
+    with tools, see the [models page](https://ollama.com/search?c=tools).
 :param response_format:
     The format for structured model outputs. The value can be:
     - None: No specific structure or format is applied to the response. The response is returned as-is.
@@ -253,7 +253,7 @@ Deserialized component.
 def run(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     *,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
@@ -268,9 +268,8 @@ Runs an Ollama Model on a given chat history.
 These are merged on top of the instance-level `generation_kwargs`.
 Optional arguments to pass to the Ollama generation endpoint, such as temperature, top_p, etc. See the
 [Ollama docs](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance. If set, it will override the `tools` parameter set
-during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callable to receive `StreamingChunk` objects as they
 arrive.  Supplying a callback (here or in the constructor) switches
 the component into streaming mode.
@@ -289,7 +288,7 @@ A dictionary with the following keys:
 async def run_async(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     *,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
@@ -302,7 +301,7 @@ Async version of run. Runs an Ollama Model on a given chat history.
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `generation_kwargs`: Per-call overrides for Ollama inference options.
 These are merged on top of the instance-level `generation_kwargs`.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
 If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callable to receive `StreamingChunk` objects as they arrive.
 Supplying a callback switches the component into streaming mode.
diff --git a/docs-website/reference_versioned_docs/version-2.17/qdrant.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/qdrant.md
similarity index 99%
rename from docs-website/reference_versioned_docs/version-2.17/qdrant.md
rename to docs-website/reference_versioned_docs/version-2.17/integrations-api/qdrant.md
index 2d9436ce42..6846a84d13 100644
--- a/docs-website/reference_versioned_docs/version-2.17/qdrant.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/qdrant.md
@@ -1038,4 +1038,3 @@ new_document_store = QdrantDocumentStore(url="http://localhost:6333",
 
 - `old_document_store`: The existing QdrantDocumentStore instance to migrate from.
 - `new_index`: The name of the new index/collection to create with sparse embeddings support.
-
diff --git a/docs-website/reference_versioned_docs/version-2.17/integrations-api/snowflake.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/snowflake.md
index 064d8a9e5c..45e4f73551 100644
--- a/docs-website/reference_versioned_docs/version-2.17/integrations-api/snowflake.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/snowflake.md
@@ -32,6 +32,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### Key-pair Authentication (MFA):
@@ -46,6 +47,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### OAuth Authentication (MFA):
@@ -61,6 +63,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### Running queries:
@@ -93,17 +96,23 @@ shape: (3, 3)
 ```python
 def __init__(user: str,
              account: str,
-             authenticator: Literal["SNOWFLAKE", "SNOWFLAKE_JWT", "OAUTH"],
-             api_key: Optional[Secret] = None,
+             authenticator: Literal["SNOWFLAKE", "SNOWFLAKE_JWT",
+                                    "OAUTH"] = "SNOWFLAKE",
+             api_key: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_API_KEY", strict=False),
              database: Optional[str] = None,
              db_schema: Optional[str] = None,
              warehouse: Optional[str] = None,
              login_timeout: Optional[int] = 60,
              return_markdown: bool = True,
-             private_key_file: Optional[Secret] = None,
-             private_key_file_pwd: Optional[Secret] = None,
-             oauth_client_id: Optional[Secret] = None,
-             oauth_client_secret: Optional[Secret] = None,
+             private_key_file: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_PRIVATE_KEY_FILE", strict=False),
+             private_key_file_pwd: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_PRIVATE_KEY_PWD", strict=False),
+             oauth_client_id: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_OAUTH_CLIENT_ID", strict=False),
+             oauth_client_secret: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_OAUTH_CLIENT_SECRET", strict=False),
              oauth_token_request_url: Optional[str] = None,
              oauth_authorization_url: Optional[str] = None) -> None
 ```
@@ -131,19 +140,15 @@ Required for OAUTH authentication.
 - `oauth_token_request_url`: OAuth token request URL for Client Credentials flow.
 - `oauth_authorization_url`: OAuth authorization URL for Authorization Code flow.
 
-<a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.test_connection"></a>
+<a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.warm_up"></a>
 
-#### SnowflakeTableRetriever.test\_connection
+#### SnowflakeTableRetriever.warm\_up
 
 ```python
-def test_connection() -> bool
+def warm_up() -> None
 ```
 
-Tests the connection with the current authentication settings.
-
-**Returns**:
-
-True if connection is successful, False otherwise.
+Warm up the component by initializing the authenticator handler and testing the database connection.
 
 <a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.to_dict"></a>
 
diff --git a/docs-website/reference_versioned_docs/version-2.17/integrations-api/together_ai.md b/docs-website/reference_versioned_docs/version-2.17/integrations-api/together_ai.md
index 8610d1ce98..5282c17ea3 100644
--- a/docs-website/reference_versioned_docs/version-2.17/integrations-api/together_ai.md
+++ b/docs-website/reference_versioned_docs/version-2.17/integrations-api/together_ai.md
@@ -231,7 +231,7 @@ def __init__(*,
              streaming_callback: Optional[StreamingCallbackT] = None,
              api_base_url: Optional[str] = "https://api.together.xyz/v1",
              generation_kwargs: Optional[Dict[str, Any]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None,
              http_client_kwargs: Optional[Dict[str, Any]] = None)
@@ -263,8 +263,8 @@ Some of the supported parameters:
     events as they become available, with the stream terminated by a data: [DONE] message.
 - `safe_prompt`: Whether to inject a safety prompt before all conversations.
 - `random_seed`: The seed to use for random sampling.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `timeout`: The timeout for the Together AI API call.
 - `max_retries`: Maximum number of retries to contact Together AI after an internal error.
 If not set, it defaults to either the `OPENAI_MAX_RETRIES` environment variable, or set to 5.
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/amazon_bedrock.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/amazon_bedrock.md
index 5bb9645dc5..a9383e8173 100644
--- a/docs-website/reference_versioned_docs/version-2.18/integrations-api/amazon_bedrock.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/amazon_bedrock.md
@@ -1135,7 +1135,7 @@ def __init__(
         generation_kwargs: Optional[Dict[str, Any]] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
         boto3_config: Optional[Dict[str, Any]] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None,
+        tools: Optional[ToolsType] = None,
         *,
         guardrail_config: Optional[Dict[str, str]] = None) -> None
 ```
@@ -1168,7 +1168,8 @@ function that handles the streaming chunks. The callback function receives a
 [StreamingChunk](https://docs.haystack.deepset.ai/docs/data-classes#streamingchunk) object and switches
 the streaming mode on.
 - `boto3_config`: The configuration for the boto3 client.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `guardrail_config`: Optional configuration for a guardrail that has been created in Amazon Bedrock.
 This must be provided as a dictionary matching either
 [GuardrailConfiguration](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GuardrailConfiguration.html).
@@ -1227,12 +1228,10 @@ Instance of `AmazonBedrockChatGenerator`.
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-def run(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+def run(messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Executes a synchronous inference call to the Amazon Bedrock model using the Converse API.
@@ -1248,7 +1247,8 @@ Supports both standard and streaming responses depending on whether a streaming
 - `stopSequences`: List of stop sequences to stop generation.
 - `temperature`: Sampling temperature.
 - `topP`: Nucleus sampling parameter.
-- `tools`: Optional list of Tools that the model may call during execution.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 **Raises**:
 
@@ -1265,11 +1265,10 @@ A dictionary containing the model-generated replies under the `"replies"` key.
 ```python
 @component.output_types(replies=List[ChatMessage])
 async def run_async(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Executes an asynchronous inference call to the Amazon Bedrock model using the Converse API.
@@ -1285,7 +1284,8 @@ Designed for use cases where non-blocking or concurrent execution is desired.
 - `stopSequences`: List of stop sequences to stop generation.
 - `temperature`: Sampling temperature.
 - `topP`: Nucleus sampling parameter.
-- `tools`: Optional list of Tool objects or a Toolset that the model can use.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 **Raises**:
 
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/anthropic.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/anthropic.md
index 4fadc67306..0ea96cf58f 100644
--- a/docs-website/reference_versioned_docs/version-2.18/integrations-api/anthropic.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/anthropic.md
@@ -196,7 +196,7 @@ def __init__(api_key: Secret = Secret.from_env_var("ANTHROPIC_API_KEY"),
              streaming_callback: Optional[StreamingCallbackT] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              ignore_tools_thinking_messages: bool = True,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              *,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None)
@@ -231,7 +231,8 @@ Supported generation_kwargs parameters are:
 `ignore_tools_thinking_messages` is `True`, the generator will drop so-called thinking messages when tool
 use is detected. See the Anthropic [tools](https://docs.anthropic.com/en/docs/tool-use#chain-of-thought-tool-use)
 for more details.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name.
 - `timeout`: Timeout for Anthropic client calls. If not set, it defaults to the default set by the Anthropic client.
 - `max_retries`: Maximum number of retries to attempt for failed requests. If not set, it defaults to the default set by
 the Anthropic client.
@@ -275,12 +276,10 @@ The deserialized component instance.
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-def run(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+def run(messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Invokes the Anthropic API with the given messages and generation kwargs.
@@ -290,8 +289,9 @@ Invokes the Anthropic API with the given messages and generation kwargs.
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 - `generation_kwargs`: Optional arguments to pass to the Anthropic generation endpoint.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should
-have a unique name. If set, it will override the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name. If set, it will override the `tools` parameter set during component
+initialization.
 
 **Returns**:
 
@@ -305,11 +305,10 @@ A dictionary with the following keys:
 ```python
 @component.output_types(replies=List[ChatMessage])
 async def run_async(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Async version of the run method. Invokes the Anthropic API with the given messages and generation kwargs.
@@ -319,8 +318,9 @@ Async version of the run method. Invokes the Anthropic API with the given messag
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 - `generation_kwargs`: Optional arguments to pass to the Anthropic generation endpoint.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should
-have a unique name. If set, it will override the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name. If set, it will override the `tools` parameter set during component
+initialization.
 
 **Returns**:
 
@@ -392,7 +392,7 @@ def __init__(region: str,
                                                    None]] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              ignore_tools_thinking_messages: bool = True,
-             tools: Optional[List[Tool]] = None,
+             tools: Optional[ToolsType] = None,
              *,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None)
@@ -425,7 +425,8 @@ Supported generation_kwargs parameters are:
 `ignore_tools_thinking_messages` is `True`, the generator will drop so-called thinking messages when tool
 use is detected. See the Anthropic [tools](https://docs.anthropic.com/en/docs/tool-use#chain-of-thought-tool-use)
 for more details.
-- `tools`: A list of Tool objects that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name.
 - `timeout`: Timeout for Anthropic client calls. If not set, it defaults to the default set by the Anthropic client.
 - `max_retries`: Maximum number of retries to attempt for failed requests. If not set, it defaults to the default set by
 the Anthropic client.
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/cohere.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/cohere.md
index 8e1c14fb52..5062839bef 100644
--- a/docs-website/reference_versioned_docs/version-2.18/integrations-api/cohere.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/cohere.md
@@ -745,7 +745,7 @@ def __init__(api_key: Secret = Secret.from_env_var(
              streaming_callback: Optional[StreamingCallbackT] = None,
              api_base_url: Optional[str] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              **kwargs: Any)
 ```
 
@@ -770,7 +770,8 @@ Some of the parameters are:
   `accurate` results or `fast` results.
 - 'temperature': A non-negative float that tunes the degree of randomness in generation. Lower temperatures
   mean less random generations.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset that the model can use.
+Each tool should have a unique name.
 
 <a id="haystack_integrations.components.generators.cohere.chat.chat_generator.CohereChatGenerator.to_dict"></a>
 
@@ -814,7 +815,7 @@ Deserialized component.
 def run(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
 ```
@@ -828,8 +829,8 @@ Invoke the chat endpoint based on the provided messages and generation parameter
 potentially override the parameters passed in the __init__ method.
 For more details on the parameters supported by the Cohere API, refer to the
 Cohere [documentation](https://docs.cohere.com/reference/chat).
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. If set, it will override
-the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 The callback function accepts StreamingChunk as an argument.
 
@@ -847,7 +848,7 @@ A dictionary with the following keys:
 async def run_async(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
 ```
@@ -861,8 +862,8 @@ Asynchronously invoke the chat endpoint based on the provided messages and gener
 potentially override the parameters passed in the __init__ method.
 For more details on the parameters supported by the Cohere API, refer to the
 Cohere [documentation](https://docs.cohere.com/reference/chat).
-- `tools`: A list of tools for which the model can prepare calls. If set, it will override
-the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 
 **Returns**:
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/google_genai.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/google_genai.md
index bd51dae768..e9cc647592 100644
--- a/docs-website/reference_versioned_docs/version-2.18/integrations-api/google_genai.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/google_genai.md
@@ -131,7 +131,7 @@ def __init__(*,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              safety_settings: Optional[List[Dict[str, Any]]] = None,
              streaming_callback: Optional[StreamingCallbackT] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None)
+             tools: Optional[ToolsType] = None)
 ```
 
 Initialize a GoogleGenAIChatGenerator instance.
@@ -156,7 +156,8 @@ For Gemini 2.5 series, supports `thinking_budget` to configure thinking behavior
   - Positive integer: Set explicit budget
 - `safety_settings`: Safety settings for content filtering
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 <a id="haystack_integrations.components.generators.google_genai.chat.chat_generator.GoogleGenAIChatGenerator.to_dict"></a>
 
@@ -201,7 +202,7 @@ def run(messages: List[ChatMessage],
         generation_kwargs: Optional[Dict[str, Any]] = None,
         safety_settings: Optional[List[Dict[str, Any]]] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None) -> Dict[str, Any]
+        tools: Optional[ToolsType] = None) -> Dict[str, Any]
 ```
 
 Run the Google Gen AI chat generator on the given input data.
@@ -215,8 +216,8 @@ the default config. Supports `thinking_budget` for Gemini 2.5 series thinking co
 default settings.
 - `streaming_callback`: A callback function that is called when a new token is
 received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. If provided, it will
-override the tools set during initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If provided, it will override the tools set during initialization.
 
 **Raises**:
 
@@ -235,12 +236,11 @@ A dictionary with the following keys:
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-async def run_async(
-        messages: List[ChatMessage],
-        generation_kwargs: Optional[Dict[str, Any]] = None,
-        safety_settings: Optional[List[Dict[str, Any]]] = None,
-        streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None) -> Dict[str, Any]
+async def run_async(messages: List[ChatMessage],
+                    generation_kwargs: Optional[Dict[str, Any]] = None,
+                    safety_settings: Optional[List[Dict[str, Any]]] = None,
+                    streaming_callback: Optional[StreamingCallbackT] = None,
+                    tools: Optional[ToolsType] = None) -> Dict[str, Any]
 ```
 
 Async version of the run method. Run the Google Gen AI chat generator on the given input data.
@@ -255,8 +255,8 @@ See https://ai.google.dev/gemini-api/docs/thinking for possible values.
 default settings.
 - `streaming_callback`: A callback function that is called when a new token is
 received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. If provided, it will
-override the tools set during initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If provided, it will override the tools set during initialization.
 
 **Raises**:
 
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/mcp.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/mcp.md
index fb75acbede..73471fc22c 100644
--- a/docs-website/reference_versioned_docs/version-2.18/integrations-api/mcp.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/mcp.md
@@ -673,7 +673,8 @@ def __init__(name: str,
              server_info: MCPServerInfo,
              description: str | None = None,
              connection_timeout: int = 30,
-             invocation_timeout: int = 30)
+             invocation_timeout: int = 30,
+             eager_connect: bool = False)
 ```
 
 Initialize the MCP tool.
@@ -685,6 +686,9 @@ Initialize the MCP tool.
 - `description`: Custom description (if None, server description will be used)
 - `connection_timeout`: Timeout in seconds for server connection
 - `invocation_timeout`: Default timeout in seconds for tool invocations
+- `eager_connect`: If True, connect to server during initialization.
+If False (default), defer connection until warm_up or first tool use,
+whichever comes first.
 
 **Raises**:
 
@@ -715,6 +719,16 @@ Asynchronous tool invocation.
 
 JSON string representation of the tool invocation result
 
+<a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.warm_up"></a>
+
+#### MCPTool.warm\_up
+
+```python
+def warm_up() -> None
+```
+
+Connect and fetch the tool schema if eager_connect is turned off.
+
 <a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.to_dict"></a>
 
 #### MCPTool.to\_dict
@@ -792,20 +806,6 @@ def tool_spec() -> dict[str, Any]
 
 Return the Tool specification to be used by the Language Model.
 
-<a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.warm_up"></a>
-
-#### MCPTool.warm\_up
-
-```python
-def warm_up() -> None
-```
-
-Prepare the Tool for use.
-
-Override this method to establish connections to remote services, load models,
-or perform other resource-intensive initialization. This method should be idempotent,
-as it may be called multiple times.
-
 <a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.invoke"></a>
 
 #### MCPTool.invoke
@@ -953,7 +953,8 @@ sse_toolset = MCPToolset(
 def __init__(server_info: MCPServerInfo,
              tool_names: list[str] | None = None,
              connection_timeout: float = 30.0,
-             invocation_timeout: float = 30.0)
+             invocation_timeout: float = 30.0,
+             eager_connect: bool = False)
 ```
 
 Initialize the MCP toolset.
@@ -965,11 +966,27 @@ Initialize the MCP toolset.
 matching names will be added to the toolset.
 - `connection_timeout`: Timeout in seconds for server connection
 - `invocation_timeout`: Default timeout in seconds for tool invocations
+- `eager_connect`: If True, connect to server and load tools during initialization.
+If False (default), defer connection to warm_up.
 
 **Raises**:
 
 - `MCPToolNotFoundError`: If any of the specified tool names are not found on the server
 
+<a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.warm_up"></a>
+
+#### MCPToolset.warm\_up
+
+```python
+def warm_up() -> None
+```
+
+Connect and load tools when eager_connect is turned off.
+
+This method is automatically called by ``ToolInvoker.warm_up()`` and ``Pipeline.warm_up()``.
+You can also call it directly before using the toolset to ensure all tool schemas
+are available without performing a real invocation.
+
 <a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.to_dict"></a>
 
 #### MCPToolset.to\_dict
@@ -1063,19 +1080,6 @@ Supports checking by:
 
 True if contained, False otherwise
 
-<a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.warm_up"></a>
-
-#### MCPToolset.warm\_up
-
-```python
-def warm_up() -> None
-```
-
-Prepare the Toolset for use.
-
-Override this method to set up shared resources like database connections or HTTP sessions.
-This method should be idempotent, as it may be called multiple times.
-
 <a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.add"></a>
 
 #### MCPToolset.add
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/mistral.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/mistral.md
index 432d8380f7..758ca5d2a8 100644
--- a/docs-website/reference_versioned_docs/version-2.18/integrations-api/mistral.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/mistral.md
@@ -382,8 +382,8 @@ Some of the supported parameters:
     Notes:
     - For structured outputs with streaming,
       the `response_format` must be a JSON schema and not a Pydantic model.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `timeout`: The timeout for the Mistral API call. If not set, it defaults to either the `OPENAI_TIMEOUT`
 environment variable, or 30 seconds.
 - `max_retries`: Maximum number of retries to contact OpenAI after an internal error.
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/ollama.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/ollama.md
index 1ba5c76aca..eefffbd938 100644
--- a/docs-website/reference_versioned_docs/version-2.18/integrations-api/ollama.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/ollama.md
@@ -165,7 +165,7 @@ def __init__(model: str = "qwen3:0.6b",
              keep_alive: Optional[Union[float, str]] = None,
              streaming_callback: Optional[Callable[[StreamingChunk],
                                                    None]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              response_format: Optional[Union[None, Literal["json"],
                                              JsonSchemaValue]] = None,
              think: Union[bool, Literal["low", "medium", "high"]] = False)
@@ -200,9 +200,9 @@ The name of the model to use. The model must already be present (pulled) in the
     A callback function that is called when a new token is received from the stream.
     The callback function accepts StreamingChunk as an argument.
 :param tools:
-    A list of `haystack.tools.Tool` or a `haystack.tools.Toolset`. Duplicate tool names raise a `ValueError`.
-    Not all models support tools. For a list of models compatible with tools, see the
-    [models page](https://ollama.com/search?c=tools).
+    A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+    Each tool should have a unique name. Not all models support tools. For a list of models compatible
+    with tools, see the [models page](https://ollama.com/search?c=tools).
 :param response_format:
     The format for structured model outputs. The value can be:
     - None: No specific structure or format is applied to the response. The response is returned as-is.
@@ -253,7 +253,7 @@ Deserialized component.
 def run(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     *,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
@@ -268,9 +268,8 @@ Runs an Ollama Model on a given chat history.
 These are merged on top of the instance-level `generation_kwargs`.
 Optional arguments to pass to the Ollama generation endpoint, such as temperature, top_p, etc. See the
 [Ollama docs](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance. If set, it will override the `tools` parameter set
-during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callable to receive `StreamingChunk` objects as they
 arrive.  Supplying a callback (here or in the constructor) switches
 the component into streaming mode.
@@ -289,7 +288,7 @@ A dictionary with the following keys:
 async def run_async(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     *,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
@@ -302,7 +301,7 @@ Async version of run. Runs an Ollama Model on a given chat history.
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `generation_kwargs`: Per-call overrides for Ollama inference options.
 These are merged on top of the instance-level `generation_kwargs`.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
 If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callable to receive `StreamingChunk` objects as they arrive.
 Supplying a callback switches the component into streaming mode.
diff --git a/docs-website/reference_versioned_docs/version-2.18/qdrant.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/qdrant.md
similarity index 99%
rename from docs-website/reference_versioned_docs/version-2.18/qdrant.md
rename to docs-website/reference_versioned_docs/version-2.18/integrations-api/qdrant.md
index 2d9436ce42..6846a84d13 100644
--- a/docs-website/reference_versioned_docs/version-2.18/qdrant.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/qdrant.md
@@ -1038,4 +1038,3 @@ new_document_store = QdrantDocumentStore(url="http://localhost:6333",
 
 - `old_document_store`: The existing QdrantDocumentStore instance to migrate from.
 - `new_index`: The name of the new index/collection to create with sparse embeddings support.
-
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/snowflake.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/snowflake.md
index 064d8a9e5c..45e4f73551 100644
--- a/docs-website/reference_versioned_docs/version-2.18/integrations-api/snowflake.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/snowflake.md
@@ -32,6 +32,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### Key-pair Authentication (MFA):
@@ -46,6 +47,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### OAuth Authentication (MFA):
@@ -61,6 +63,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### Running queries:
@@ -93,17 +96,23 @@ shape: (3, 3)
 ```python
 def __init__(user: str,
              account: str,
-             authenticator: Literal["SNOWFLAKE", "SNOWFLAKE_JWT", "OAUTH"],
-             api_key: Optional[Secret] = None,
+             authenticator: Literal["SNOWFLAKE", "SNOWFLAKE_JWT",
+                                    "OAUTH"] = "SNOWFLAKE",
+             api_key: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_API_KEY", strict=False),
              database: Optional[str] = None,
              db_schema: Optional[str] = None,
              warehouse: Optional[str] = None,
              login_timeout: Optional[int] = 60,
              return_markdown: bool = True,
-             private_key_file: Optional[Secret] = None,
-             private_key_file_pwd: Optional[Secret] = None,
-             oauth_client_id: Optional[Secret] = None,
-             oauth_client_secret: Optional[Secret] = None,
+             private_key_file: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_PRIVATE_KEY_FILE", strict=False),
+             private_key_file_pwd: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_PRIVATE_KEY_PWD", strict=False),
+             oauth_client_id: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_OAUTH_CLIENT_ID", strict=False),
+             oauth_client_secret: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_OAUTH_CLIENT_SECRET", strict=False),
              oauth_token_request_url: Optional[str] = None,
              oauth_authorization_url: Optional[str] = None) -> None
 ```
@@ -131,19 +140,15 @@ Required for OAUTH authentication.
 - `oauth_token_request_url`: OAuth token request URL for Client Credentials flow.
 - `oauth_authorization_url`: OAuth authorization URL for Authorization Code flow.
 
-<a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.test_connection"></a>
+<a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.warm_up"></a>
 
-#### SnowflakeTableRetriever.test\_connection
+#### SnowflakeTableRetriever.warm\_up
 
 ```python
-def test_connection() -> bool
+def warm_up() -> None
 ```
 
-Tests the connection with the current authentication settings.
-
-**Returns**:
-
-True if connection is successful, False otherwise.
+Warm up the component by initializing the authenticator handler and testing the database connection.
 
 <a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.to_dict"></a>
 
diff --git a/docs-website/reference_versioned_docs/version-2.18/integrations-api/together_ai.md b/docs-website/reference_versioned_docs/version-2.18/integrations-api/together_ai.md
index 8610d1ce98..5282c17ea3 100644
--- a/docs-website/reference_versioned_docs/version-2.18/integrations-api/together_ai.md
+++ b/docs-website/reference_versioned_docs/version-2.18/integrations-api/together_ai.md
@@ -231,7 +231,7 @@ def __init__(*,
              streaming_callback: Optional[StreamingCallbackT] = None,
              api_base_url: Optional[str] = "https://api.together.xyz/v1",
              generation_kwargs: Optional[Dict[str, Any]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None,
              http_client_kwargs: Optional[Dict[str, Any]] = None)
@@ -263,8 +263,8 @@ Some of the supported parameters:
     events as they become available, with the stream terminated by a data: [DONE] message.
 - `safe_prompt`: Whether to inject a safety prompt before all conversations.
 - `random_seed`: The seed to use for random sampling.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `timeout`: The timeout for the Together AI API call.
 - `max_retries`: Maximum number of retries to contact Together AI after an internal error.
 If not set, it defaults to either the `OPENAI_MAX_RETRIES` environment variable, or set to 5.
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/amazon_bedrock.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/amazon_bedrock.md
index 5bb9645dc5..a9383e8173 100644
--- a/docs-website/reference_versioned_docs/version-2.19/integrations-api/amazon_bedrock.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/amazon_bedrock.md
@@ -1135,7 +1135,7 @@ def __init__(
         generation_kwargs: Optional[Dict[str, Any]] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
         boto3_config: Optional[Dict[str, Any]] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None,
+        tools: Optional[ToolsType] = None,
         *,
         guardrail_config: Optional[Dict[str, str]] = None) -> None
 ```
@@ -1168,7 +1168,8 @@ function that handles the streaming chunks. The callback function receives a
 [StreamingChunk](https://docs.haystack.deepset.ai/docs/data-classes#streamingchunk) object and switches
 the streaming mode on.
 - `boto3_config`: The configuration for the boto3 client.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `guardrail_config`: Optional configuration for a guardrail that has been created in Amazon Bedrock.
 This must be provided as a dictionary matching either
 [GuardrailConfiguration](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GuardrailConfiguration.html).
@@ -1227,12 +1228,10 @@ Instance of `AmazonBedrockChatGenerator`.
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-def run(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+def run(messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Executes a synchronous inference call to the Amazon Bedrock model using the Converse API.
@@ -1248,7 +1247,8 @@ Supports both standard and streaming responses depending on whether a streaming
 - `stopSequences`: List of stop sequences to stop generation.
 - `temperature`: Sampling temperature.
 - `topP`: Nucleus sampling parameter.
-- `tools`: Optional list of Tools that the model may call during execution.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 **Raises**:
 
@@ -1265,11 +1265,10 @@ A dictionary containing the model-generated replies under the `"replies"` key.
 ```python
 @component.output_types(replies=List[ChatMessage])
 async def run_async(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Executes an asynchronous inference call to the Amazon Bedrock model using the Converse API.
@@ -1285,7 +1284,8 @@ Designed for use cases where non-blocking or concurrent execution is desired.
 - `stopSequences`: List of stop sequences to stop generation.
 - `temperature`: Sampling temperature.
 - `topP`: Nucleus sampling parameter.
-- `tools`: Optional list of Tool objects or a Toolset that the model can use.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 **Raises**:
 
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/anthropic.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/anthropic.md
index 4fadc67306..0ea96cf58f 100644
--- a/docs-website/reference_versioned_docs/version-2.19/integrations-api/anthropic.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/anthropic.md
@@ -196,7 +196,7 @@ def __init__(api_key: Secret = Secret.from_env_var("ANTHROPIC_API_KEY"),
              streaming_callback: Optional[StreamingCallbackT] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              ignore_tools_thinking_messages: bool = True,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              *,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None)
@@ -231,7 +231,8 @@ Supported generation_kwargs parameters are:
 `ignore_tools_thinking_messages` is `True`, the generator will drop so-called thinking messages when tool
 use is detected. See the Anthropic [tools](https://docs.anthropic.com/en/docs/tool-use#chain-of-thought-tool-use)
 for more details.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name.
 - `timeout`: Timeout for Anthropic client calls. If not set, it defaults to the default set by the Anthropic client.
 - `max_retries`: Maximum number of retries to attempt for failed requests. If not set, it defaults to the default set by
 the Anthropic client.
@@ -275,12 +276,10 @@ The deserialized component instance.
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-def run(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+def run(messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Invokes the Anthropic API with the given messages and generation kwargs.
@@ -290,8 +289,9 @@ Invokes the Anthropic API with the given messages and generation kwargs.
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 - `generation_kwargs`: Optional arguments to pass to the Anthropic generation endpoint.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should
-have a unique name. If set, it will override the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name. If set, it will override the `tools` parameter set during component
+initialization.
 
 **Returns**:
 
@@ -305,11 +305,10 @@ A dictionary with the following keys:
 ```python
 @component.output_types(replies=List[ChatMessage])
 async def run_async(
-    messages: List[ChatMessage],
-    streaming_callback: Optional[StreamingCallbackT] = None,
-    generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None
-) -> Dict[str, List[ChatMessage]]
+        messages: List[ChatMessage],
+        streaming_callback: Optional[StreamingCallbackT] = None,
+        generation_kwargs: Optional[Dict[str, Any]] = None,
+        tools: Optional[ToolsType] = None) -> Dict[str, List[ChatMessage]]
 ```
 
 Async version of the run method. Invokes the Anthropic API with the given messages and generation kwargs.
@@ -319,8 +318,9 @@ Async version of the run method. Invokes the Anthropic API with the given messag
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 - `generation_kwargs`: Optional arguments to pass to the Anthropic generation endpoint.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should
-have a unique name. If set, it will override the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name. If set, it will override the `tools` parameter set during component
+initialization.
 
 **Returns**:
 
@@ -392,7 +392,7 @@ def __init__(region: str,
                                                    None]] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              ignore_tools_thinking_messages: bool = True,
-             tools: Optional[List[Tool]] = None,
+             tools: Optional[ToolsType] = None,
              *,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None)
@@ -425,7 +425,8 @@ Supported generation_kwargs parameters are:
 `ignore_tools_thinking_messages` is `True`, the generator will drop so-called thinking messages when tool
 use is detected. See the Anthropic [tools](https://docs.anthropic.com/en/docs/tool-use#chain-of-thought-tool-use)
 for more details.
-- `tools`: A list of Tool objects that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset, that the model can use.
+Each tool should have a unique name.
 - `timeout`: Timeout for Anthropic client calls. If not set, it defaults to the default set by the Anthropic client.
 - `max_retries`: Maximum number of retries to attempt for failed requests. If not set, it defaults to the default set by
 the Anthropic client.
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/cohere.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/cohere.md
index 8e1c14fb52..5062839bef 100644
--- a/docs-website/reference_versioned_docs/version-2.19/integrations-api/cohere.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/cohere.md
@@ -745,7 +745,7 @@ def __init__(api_key: Secret = Secret.from_env_var(
              streaming_callback: Optional[StreamingCallbackT] = None,
              api_base_url: Optional[str] = None,
              generation_kwargs: Optional[Dict[str, Any]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              **kwargs: Any)
 ```
 
@@ -770,7 +770,8 @@ Some of the parameters are:
   `accurate` results or `fast` results.
 - 'temperature': A non-negative float that tunes the degree of randomness in generation. Lower temperatures
   mean less random generations.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset that the model can use.
+Each tool should have a unique name.
 
 <a id="haystack_integrations.components.generators.cohere.chat.chat_generator.CohereChatGenerator.to_dict"></a>
 
@@ -814,7 +815,7 @@ Deserialized component.
 def run(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
 ```
@@ -828,8 +829,8 @@ Invoke the chat endpoint based on the provided messages and generation parameter
 potentially override the parameters passed in the __init__ method.
 For more details on the parameters supported by the Cohere API, refer to the
 Cohere [documentation](https://docs.cohere.com/reference/chat).
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. If set, it will override
-the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 The callback function accepts StreamingChunk as an argument.
 
@@ -847,7 +848,7 @@ A dictionary with the following keys:
 async def run_async(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
 ```
@@ -861,8 +862,8 @@ Asynchronously invoke the chat endpoint based on the provided messages and gener
 potentially override the parameters passed in the __init__ method.
 For more details on the parameters supported by the Cohere API, refer to the
 Cohere [documentation](https://docs.cohere.com/reference/chat).
-- `tools`: A list of tools for which the model can prepare calls. If set, it will override
-the `tools` parameter set during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
 
 **Returns**:
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/google_genai.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/google_genai.md
index bd51dae768..e9cc647592 100644
--- a/docs-website/reference_versioned_docs/version-2.19/integrations-api/google_genai.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/google_genai.md
@@ -131,7 +131,7 @@ def __init__(*,
              generation_kwargs: Optional[Dict[str, Any]] = None,
              safety_settings: Optional[List[Dict[str, Any]]] = None,
              streaming_callback: Optional[StreamingCallbackT] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None)
+             tools: Optional[ToolsType] = None)
 ```
 
 Initialize a GoogleGenAIChatGenerator instance.
@@ -156,7 +156,8 @@ For Gemini 2.5 series, supports `thinking_budget` to configure thinking behavior
   - Positive integer: Set explicit budget
 - `safety_settings`: Safety settings for content filtering
 - `streaming_callback`: A callback function that is called when a new token is received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. Each tool should have a unique name.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 
 <a id="haystack_integrations.components.generators.google_genai.chat.chat_generator.GoogleGenAIChatGenerator.to_dict"></a>
 
@@ -201,7 +202,7 @@ def run(messages: List[ChatMessage],
         generation_kwargs: Optional[Dict[str, Any]] = None,
         safety_settings: Optional[List[Dict[str, Any]]] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None) -> Dict[str, Any]
+        tools: Optional[ToolsType] = None) -> Dict[str, Any]
 ```
 
 Run the Google Gen AI chat generator on the given input data.
@@ -215,8 +216,8 @@ the default config. Supports `thinking_budget` for Gemini 2.5 series thinking co
 default settings.
 - `streaming_callback`: A callback function that is called when a new token is
 received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. If provided, it will
-override the tools set during initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If provided, it will override the tools set during initialization.
 
 **Raises**:
 
@@ -235,12 +236,11 @@ A dictionary with the following keys:
 
 ```python
 @component.output_types(replies=List[ChatMessage])
-async def run_async(
-        messages: List[ChatMessage],
-        generation_kwargs: Optional[Dict[str, Any]] = None,
-        safety_settings: Optional[List[Dict[str, Any]]] = None,
-        streaming_callback: Optional[StreamingCallbackT] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None) -> Dict[str, Any]
+async def run_async(messages: List[ChatMessage],
+                    generation_kwargs: Optional[Dict[str, Any]] = None,
+                    safety_settings: Optional[List[Dict[str, Any]]] = None,
+                    streaming_callback: Optional[StreamingCallbackT] = None,
+                    tools: Optional[ToolsType] = None) -> Dict[str, Any]
 ```
 
 Async version of the run method. Run the Google Gen AI chat generator on the given input data.
@@ -255,8 +255,8 @@ See https://ai.google.dev/gemini-api/docs/thinking for possible values.
 default settings.
 - `streaming_callback`: A callback function that is called when a new token is
 received from the stream.
-- `tools`: A list of Tool objects or a Toolset that the model can use. If provided, it will
-override the tools set during initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If provided, it will override the tools set during initialization.
 
 **Raises**:
 
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/mcp.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/mcp.md
index fb75acbede..73471fc22c 100644
--- a/docs-website/reference_versioned_docs/version-2.19/integrations-api/mcp.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/mcp.md
@@ -673,7 +673,8 @@ def __init__(name: str,
              server_info: MCPServerInfo,
              description: str | None = None,
              connection_timeout: int = 30,
-             invocation_timeout: int = 30)
+             invocation_timeout: int = 30,
+             eager_connect: bool = False)
 ```
 
 Initialize the MCP tool.
@@ -685,6 +686,9 @@ Initialize the MCP tool.
 - `description`: Custom description (if None, server description will be used)
 - `connection_timeout`: Timeout in seconds for server connection
 - `invocation_timeout`: Default timeout in seconds for tool invocations
+- `eager_connect`: If True, connect to server during initialization.
+If False (default), defer connection until warm_up or first tool use,
+whichever comes first.
 
 **Raises**:
 
@@ -715,6 +719,16 @@ Asynchronous tool invocation.
 
 JSON string representation of the tool invocation result
 
+<a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.warm_up"></a>
+
+#### MCPTool.warm\_up
+
+```python
+def warm_up() -> None
+```
+
+Connect and fetch the tool schema if eager_connect is turned off.
+
 <a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.to_dict"></a>
 
 #### MCPTool.to\_dict
@@ -792,20 +806,6 @@ def tool_spec() -> dict[str, Any]
 
 Return the Tool specification to be used by the Language Model.
 
-<a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.warm_up"></a>
-
-#### MCPTool.warm\_up
-
-```python
-def warm_up() -> None
-```
-
-Prepare the Tool for use.
-
-Override this method to establish connections to remote services, load models,
-or perform other resource-intensive initialization. This method should be idempotent,
-as it may be called multiple times.
-
 <a id="haystack_integrations.tools.mcp.mcp_tool.MCPTool.invoke"></a>
 
 #### MCPTool.invoke
@@ -953,7 +953,8 @@ sse_toolset = MCPToolset(
 def __init__(server_info: MCPServerInfo,
              tool_names: list[str] | None = None,
              connection_timeout: float = 30.0,
-             invocation_timeout: float = 30.0)
+             invocation_timeout: float = 30.0,
+             eager_connect: bool = False)
 ```
 
 Initialize the MCP toolset.
@@ -965,11 +966,27 @@ Initialize the MCP toolset.
 matching names will be added to the toolset.
 - `connection_timeout`: Timeout in seconds for server connection
 - `invocation_timeout`: Default timeout in seconds for tool invocations
+- `eager_connect`: If True, connect to server and load tools during initialization.
+If False (default), defer connection to warm_up.
 
 **Raises**:
 
 - `MCPToolNotFoundError`: If any of the specified tool names are not found on the server
 
+<a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.warm_up"></a>
+
+#### MCPToolset.warm\_up
+
+```python
+def warm_up() -> None
+```
+
+Connect and load tools when eager_connect is turned off.
+
+This method is automatically called by ``ToolInvoker.warm_up()`` and ``Pipeline.warm_up()``.
+You can also call it directly before using the toolset to ensure all tool schemas
+are available without performing a real invocation.
+
 <a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.to_dict"></a>
 
 #### MCPToolset.to\_dict
@@ -1063,19 +1080,6 @@ Supports checking by:
 
 True if contained, False otherwise
 
-<a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.warm_up"></a>
-
-#### MCPToolset.warm\_up
-
-```python
-def warm_up() -> None
-```
-
-Prepare the Toolset for use.
-
-Override this method to set up shared resources like database connections or HTTP sessions.
-This method should be idempotent, as it may be called multiple times.
-
 <a id="haystack_integrations.tools.mcp.mcp_toolset.MCPToolset.add"></a>
 
 #### MCPToolset.add
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/mistral.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/mistral.md
index 432d8380f7..758ca5d2a8 100644
--- a/docs-website/reference_versioned_docs/version-2.19/integrations-api/mistral.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/mistral.md
@@ -382,8 +382,8 @@ Some of the supported parameters:
     Notes:
     - For structured outputs with streaming,
       the `response_format` must be a JSON schema and not a Pydantic model.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `timeout`: The timeout for the Mistral API call. If not set, it defaults to either the `OPENAI_TIMEOUT`
 environment variable, or 30 seconds.
 - `max_retries`: Maximum number of retries to contact OpenAI after an internal error.
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/ollama.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/ollama.md
index 1ba5c76aca..eefffbd938 100644
--- a/docs-website/reference_versioned_docs/version-2.19/integrations-api/ollama.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/ollama.md
@@ -165,7 +165,7 @@ def __init__(model: str = "qwen3:0.6b",
              keep_alive: Optional[Union[float, str]] = None,
              streaming_callback: Optional[Callable[[StreamingChunk],
                                                    None]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              response_format: Optional[Union[None, Literal["json"],
                                              JsonSchemaValue]] = None,
              think: Union[bool, Literal["low", "medium", "high"]] = False)
@@ -200,9 +200,9 @@ The name of the model to use. The model must already be present (pulled) in the
     A callback function that is called when a new token is received from the stream.
     The callback function accepts StreamingChunk as an argument.
 :param tools:
-    A list of `haystack.tools.Tool` or a `haystack.tools.Toolset`. Duplicate tool names raise a `ValueError`.
-    Not all models support tools. For a list of models compatible with tools, see the
-    [models page](https://ollama.com/search?c=tools).
+    A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+    Each tool should have a unique name. Not all models support tools. For a list of models compatible
+    with tools, see the [models page](https://ollama.com/search?c=tools).
 :param response_format:
     The format for structured model outputs. The value can be:
     - None: No specific structure or format is applied to the response. The response is returned as-is.
@@ -253,7 +253,7 @@ Deserialized component.
 def run(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     *,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
@@ -268,9 +268,8 @@ Runs an Ollama Model on a given chat history.
 These are merged on top of the instance-level `generation_kwargs`.
 Optional arguments to pass to the Ollama generation endpoint, such as temperature, top_p, etc. See the
 [Ollama docs](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance. If set, it will override the `tools` parameter set
-during component initialization.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callable to receive `StreamingChunk` objects as they
 arrive.  Supplying a callback (here or in the constructor) switches
 the component into streaming mode.
@@ -289,7 +288,7 @@ A dictionary with the following keys:
 async def run_async(
     messages: List[ChatMessage],
     generation_kwargs: Optional[Dict[str, Any]] = None,
-    tools: Optional[Union[List[Tool], Toolset]] = None,
+    tools: Optional[ToolsType] = None,
     *,
     streaming_callback: Optional[StreamingCallbackT] = None
 ) -> Dict[str, List[ChatMessage]]
@@ -302,7 +301,7 @@ Async version of run. Runs an Ollama Model on a given chat history.
 - `messages`: A list of ChatMessage instances representing the input messages.
 - `generation_kwargs`: Per-call overrides for Ollama inference options.
 These are merged on top of the instance-level `generation_kwargs`.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
 If set, it will override the `tools` parameter set during component initialization.
 - `streaming_callback`: A callable to receive `StreamingChunk` objects as they arrive.
 Supplying a callback switches the component into streaming mode.
diff --git a/docs-website/reference_versioned_docs/version-2.19/qdrant.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/qdrant.md
similarity index 99%
rename from docs-website/reference_versioned_docs/version-2.19/qdrant.md
rename to docs-website/reference_versioned_docs/version-2.19/integrations-api/qdrant.md
index 2d9436ce42..6846a84d13 100644
--- a/docs-website/reference_versioned_docs/version-2.19/qdrant.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/qdrant.md
@@ -1038,4 +1038,3 @@ new_document_store = QdrantDocumentStore(url="http://localhost:6333",
 
 - `old_document_store`: The existing QdrantDocumentStore instance to migrate from.
 - `new_index`: The name of the new index/collection to create with sparse embeddings support.
-
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/snowflake.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/snowflake.md
index 064d8a9e5c..45e4f73551 100644
--- a/docs-website/reference_versioned_docs/version-2.19/integrations-api/snowflake.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/snowflake.md
@@ -32,6 +32,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### Key-pair Authentication (MFA):
@@ -46,6 +47,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### OAuth Authentication (MFA):
@@ -61,6 +63,7 @@ executor = SnowflakeTableRetriever(
     db_schema="<SCHEMA-NAME>",
     warehouse="<WAREHOUSE-NAME>",
 )
+executor.warm_up()
 ```
 
 #### Running queries:
@@ -93,17 +96,23 @@ shape: (3, 3)
 ```python
 def __init__(user: str,
              account: str,
-             authenticator: Literal["SNOWFLAKE", "SNOWFLAKE_JWT", "OAUTH"],
-             api_key: Optional[Secret] = None,
+             authenticator: Literal["SNOWFLAKE", "SNOWFLAKE_JWT",
+                                    "OAUTH"] = "SNOWFLAKE",
+             api_key: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_API_KEY", strict=False),
              database: Optional[str] = None,
              db_schema: Optional[str] = None,
              warehouse: Optional[str] = None,
              login_timeout: Optional[int] = 60,
              return_markdown: bool = True,
-             private_key_file: Optional[Secret] = None,
-             private_key_file_pwd: Optional[Secret] = None,
-             oauth_client_id: Optional[Secret] = None,
-             oauth_client_secret: Optional[Secret] = None,
+             private_key_file: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_PRIVATE_KEY_FILE", strict=False),
+             private_key_file_pwd: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_PRIVATE_KEY_PWD", strict=False),
+             oauth_client_id: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_OAUTH_CLIENT_ID", strict=False),
+             oauth_client_secret: Optional[Secret] = Secret.from_env_var(
+                 "SNOWFLAKE_OAUTH_CLIENT_SECRET", strict=False),
              oauth_token_request_url: Optional[str] = None,
              oauth_authorization_url: Optional[str] = None) -> None
 ```
@@ -131,19 +140,15 @@ Required for OAUTH authentication.
 - `oauth_token_request_url`: OAuth token request URL for Client Credentials flow.
 - `oauth_authorization_url`: OAuth authorization URL for Authorization Code flow.
 
-<a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.test_connection"></a>
+<a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.warm_up"></a>
 
-#### SnowflakeTableRetriever.test\_connection
+#### SnowflakeTableRetriever.warm\_up
 
 ```python
-def test_connection() -> bool
+def warm_up() -> None
 ```
 
-Tests the connection with the current authentication settings.
-
-**Returns**:
-
-True if connection is successful, False otherwise.
+Warm up the component by initializing the authenticator handler and testing the database connection.
 
 <a id="haystack_integrations.components.retrievers.snowflake.snowflake_table_retriever.SnowflakeTableRetriever.to_dict"></a>
 
diff --git a/docs-website/reference_versioned_docs/version-2.19/integrations-api/together_ai.md b/docs-website/reference_versioned_docs/version-2.19/integrations-api/together_ai.md
index 8610d1ce98..5282c17ea3 100644
--- a/docs-website/reference_versioned_docs/version-2.19/integrations-api/together_ai.md
+++ b/docs-website/reference_versioned_docs/version-2.19/integrations-api/together_ai.md
@@ -231,7 +231,7 @@ def __init__(*,
              streaming_callback: Optional[StreamingCallbackT] = None,
              api_base_url: Optional[str] = "https://api.together.xyz/v1",
              generation_kwargs: Optional[Dict[str, Any]] = None,
-             tools: Optional[Union[List[Tool], Toolset]] = None,
+             tools: Optional[ToolsType] = None,
              timeout: Optional[float] = None,
              max_retries: Optional[int] = None,
              http_client_kwargs: Optional[Dict[str, Any]] = None)
@@ -263,8 +263,8 @@ Some of the supported parameters:
     events as they become available, with the stream terminated by a data: [DONE] message.
 - `safe_prompt`: Whether to inject a safety prompt before all conversations.
 - `random_seed`: The seed to use for random sampling.
-- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-list of `Tool` objects or a `Toolset` instance.
+- `tools`: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+Each tool should have a unique name.
 - `timeout`: The timeout for the Together AI API call.
 - `max_retries`: Maximum number of retries to contact Together AI after an internal error.
 If not set, it defaults to either the `OPENAI_MAX_RETRIES` environment variable, or set to 5.