first commit

agent_dhal/agentdhal_extensions/tools/mcp/__init__.py

@@ -0,0 +1,22 @@
+from ._actor import McpSessionActor
+from ._config import McpServerParams, SseServerParams, StdioServerParams, StreamableHttpServerParams
+from ._factory import mcp_server_tools
+from ._session import create_mcp_server_session
+from ._sse import SseMcpToolAdapter
+from ._stdio import StdioMcpToolAdapter
+from ._streamable_http import StreamableHttpMcpToolAdapter
+from ._workbench import McpWorkbench
+
+__all__ = [
+    "create_mcp_server_session",
+    "McpSessionActor",
+    "StdioMcpToolAdapter",
+    "StdioServerParams",
+    "SseMcpToolAdapter",
+    "SseServerParams",
+    "StreamableHttpMcpToolAdapter",
+    "StreamableHttpServerParams",
+    "McpServerParams",
+    "mcp_server_tools",
+    "McpWorkbench",
+]

agent_dhal/agentdhal_extensions/tools/mcp/_actor.py

@@ -0,0 +1,310 @@
+import asyncio
+import atexit
+import base64
+import io
+import logging
+from typing import Any, Coroutine, Dict, Mapping, TypedDict
+
+from agentdhal_core import Component, ComponentBase, ComponentModel, Image
+from agentdhal_core.models import (
+    AssistantMessage,
+    ChatCompletionClient,
+    LLMMessage,
+    ModelInfo,
+    SystemMessage,
+    UserMessage,
+)
+from PIL import Image as PILImage
+from pydantic import BaseModel
+from typing_extensions import Self
+
+from mcp import types as mcp_types
+from mcp.client.session import ClientSession
+from mcp.shared.context import RequestContext
+
+from ._config import McpServerParams
+from ._session import create_mcp_server_session
+
+logger = logging.getLogger(__name__)
+
+McpResult = (
+    Coroutine[Any, Any, mcp_types.ListToolsResult]
+    | Coroutine[Any, Any, mcp_types.CallToolResult]
+    | Coroutine[Any, Any, mcp_types.ListPromptsResult]
+    | Coroutine[Any, Any, mcp_types.ListResourcesResult]
+    | Coroutine[Any, Any, mcp_types.ListResourceTemplatesResult]
+    | Coroutine[Any, Any, mcp_types.ReadResourceResult]
+    | Coroutine[Any, Any, mcp_types.GetPromptResult]
+)
+McpFuture = asyncio.Future[McpResult]
+
+
+def _parse_sampling_content(
+    content: mcp_types.TextContent | mcp_types.ImageContent | mcp_types.AudioContent, model_info: ModelInfo
+) -> str | Image:
+    """Convert MCP content types to Autogen content types."""
+    if content.type == "text":
+        return content.text
+    elif content.type == "image":
+        if not model_info["vision"]:
+            raise ValueError("Sampling model does not support image content.")
+        # Decode base64 image data and create PIL Image
+        image_data = base64.b64decode(content.data)
+        pil_image = PILImage.open(io.BytesIO(image_data))
+        return Image.from_pil(pil_image)
+    else:
+        raise ValueError(f"Unsupported content type: {content.type}")
+
+
+def _parse_sampling_message(message: mcp_types.SamplingMessage, model_info: ModelInfo) -> LLMMessage:
+    """Convert MCP sampling messages to Autogen messages."""
+    content = _parse_sampling_content(message.content, model_info=model_info)
+    if message.role == "user":
+        return UserMessage(
+            source="user",
+            content=[content],
+        )
+    elif message.role == "assistant":
+        assert isinstance(content, str), "Assistant messages only support string content."
+        return AssistantMessage(
+            source="assistant",
+            content=content,
+        )
+    else:
+        raise ValueError(f"Unrecognized message role: {message.role}")
+
+
+class McpActorArgs(TypedDict):
+    name: str | None
+    kargs: Mapping[str, Any]
+
+
+class McpSessionActorConfig(BaseModel):
+    server_params: McpServerParams
+    model_client: ComponentModel | Dict[str, Any] | None = None
+
+
+class McpSessionActor(ComponentBase[BaseModel], Component[McpSessionActorConfig]):
+    component_type = "mcp_session_actor"
+    component_config_schema = McpSessionActorConfig
+    component_provider_override = "agentdhal_extensions.tools.mcp.McpSessionActor"
+
+    server_params: McpServerParams
+
+    # model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    def __init__(self, server_params: McpServerParams, model_client: ChatCompletionClient | None = None) -> None:
+        self.server_params: McpServerParams = server_params
+        self._model_client = model_client
+        self.name = "mcp_session_actor"
+        self.description = "MCP session actor"
+        self._command_queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue()
+        self._actor_task: asyncio.Task[Any] | None = None
+        self._shutdown_future: asyncio.Future[Any] | None = None
+        self._active = False
+        self._initialize_result: mcp_types.InitializeResult | None = None
+        atexit.register(self._sync_shutdown)
+
+    @property
+    def initialize_result(self) -> mcp_types.InitializeResult | None:
+        return self._initialize_result
+
+    async def initialize(self) -> None:
+        if not self._active:
+            self._active = True
+            self._actor_task = asyncio.create_task(self._run_actor())
+
+    async def call(self, type: str, args: McpActorArgs | None = None) -> McpFuture:
+        if not self._active:
+            raise RuntimeError("MCP Actor not running, call initialize() first")
+        if self._actor_task and self._actor_task.done():
+            raise RuntimeError("MCP actor task crashed", self._actor_task.exception())
+        fut: asyncio.Future[McpFuture] = asyncio.Future()
+        if type in {"list_tools", "list_prompts", "list_resources", "list_resource_templates", "shutdown"}:
+            await self._command_queue.put({"type": type, "future": fut})
+            res = await fut
+        elif type in {"call_tool", "read_resource", "get_prompt"}:
+            if args is None:
+                raise ValueError(f"args is required for {type}")
+            name = args.get("name", None)
+            kwargs = args.get("kargs", {})
+            if type == "call_tool" and name is None:
+                raise ValueError("name is required for call_tool")
+            elif type == "read_resource":
+                uri = kwargs.get("uri", None)
+                if uri is None:
+                    raise ValueError("uri is required for read_resource")
+                await self._command_queue.put({"type": type, "uri": uri, "future": fut})
+            elif type == "get_prompt":
+                if name is None:
+                    raise ValueError("name is required for get_prompt")
+                prompt_args = kwargs.get("arguments", None)
+                await self._command_queue.put({"type": type, "name": name, "args": prompt_args, "future": fut})
+            else:  # call_tool
+                await self._command_queue.put({"type": type, "name": name, "args": kwargs, "future": fut})
+            res = await fut
+        else:
+            raise ValueError(f"Unknown command type: {type}")
+        return res
+
+    async def close(self) -> None:
+        if not self._active or self._actor_task is None:
+            return
+        self._shutdown_future = asyncio.Future()
+        await self._command_queue.put({"type": "shutdown", "future": self._shutdown_future})
+        await self._shutdown_future
+        await self._actor_task
+        self._active = False
+
+    async def _sampling_callback(
+        self,
+        context: RequestContext[ClientSession, Any],
+        params: mcp_types.CreateMessageRequestParams,
+    ) -> mcp_types.CreateMessageResult | mcp_types.ErrorData:
+        """Handle sampling requests using the provided model client."""
+        if self._model_client is None:
+            # Return an error when no model client is available
+            return mcp_types.ErrorData(
+                code=mcp_types.INVALID_REQUEST,
+                message="No model client available for sampling.",
+                data=None,
+            )
+
+        llm_messages: list[LLMMessage] = []
+
+        try:
+            if params.systemPrompt:
+                llm_messages.append(SystemMessage(content=params.systemPrompt))
+
+            for mcp_message in params.messages:
+                llm_messages.append(_parse_sampling_message(mcp_message, model_info=self._model_client.model_info))
+
+        except Exception as e:
+            return mcp_types.ErrorData(
+                code=mcp_types.INVALID_PARAMS,
+                message="Error processing sampling messages.",
+                data=f"{type(e).__name__}: {e}",
+            )
+
+        try:
+            result = await self._model_client.create(messages=llm_messages)
+
+            content = result.content
+            if not isinstance(content, str):
+                content = str(content)
+
+            return mcp_types.CreateMessageResult(
+                role="assistant",
+                content=mcp_types.TextContent(type="text", text=content),
+                model=self._model_client.model_info["family"],
+                stopReason=result.finish_reason,
+            )
+        except Exception as e:
+            return mcp_types.ErrorData(
+                code=mcp_types.INTERNAL_ERROR,
+                message="Error sampling from model client.",
+                data=f"{type(e).__name__}: {e}",
+            )
+
+    async def _run_actor(self) -> None:
+        result: McpResult
+        try:
+            async with create_mcp_server_session(
+                self.server_params, sampling_callback=self._sampling_callback
+            ) as session:
+                # Save the initialize result
+                self._initialize_result = await session.initialize()
+                while True:
+                    cmd = await self._command_queue.get()
+                    if cmd["type"] == "shutdown":
+                        cmd["future"].set_result("ok")
+                        break
+                    elif cmd["type"] == "call_tool":
+                        try:
+                            result = session.call_tool(name=cmd["name"], arguments=cmd["args"])
+                            cmd["future"].set_result(result)
+                        except Exception as e:
+                            cmd["future"].set_exception(e)
+                    elif cmd["type"] == "read_resource":
+                        try:
+                            result = session.read_resource(uri=cmd["uri"])
+                            cmd["future"].set_result(result)
+                        except Exception as e:
+                            cmd["future"].set_exception(e)
+                    elif cmd["type"] == "get_prompt":
+                        try:
+                            result = session.get_prompt(name=cmd["name"], arguments=cmd["args"])
+                            cmd["future"].set_result(result)
+                        except Exception as e:
+                            cmd["future"].set_exception(e)
+                    elif cmd["type"] == "list_tools":
+                        try:
+                            result = session.list_tools()
+                            cmd["future"].set_result(result)
+                        except Exception as e:
+                            cmd["future"].set_exception(e)
+                    elif cmd["type"] == "list_prompts":
+                        try:
+                            result = session.list_prompts()
+                            cmd["future"].set_result(result)
+                        except Exception as e:
+                            cmd["future"].set_exception(e)
+                    elif cmd["type"] == "list_resources":
+                        try:
+                            result = session.list_resources()
+                            cmd["future"].set_result(result)
+                        except Exception as e:
+                            cmd["future"].set_exception(e)
+                    elif cmd["type"] == "list_resource_templates":
+                        try:
+                            result = session.list_resource_templates()
+                            cmd["future"].set_result(result)
+                        except Exception as e:
+                            cmd["future"].set_exception(e)
+        except Exception as e:
+            if self._shutdown_future and not self._shutdown_future.done():
+                self._shutdown_future.set_exception(e)
+            else:
+                logger.exception("Exception in MCP actor task")
+        finally:
+            self._active = False
+            self._actor_task = None
+
+    def _sync_shutdown(self) -> None:
+        if not self._active or self._actor_task is None:
+            return
+        try:
+            loop = asyncio.get_event_loop()
+        except RuntimeError:
+            # No loop available — interpreter is likely shutting down
+            return
+
+        if loop.is_closed():
+            return
+
+        if loop.is_running():
+            loop.create_task(self.close())
+        else:
+            loop.run_until_complete(self.close())
+
+    def _to_config(self) -> McpSessionActorConfig:
+        """
+        Convert the adapter to its configuration representation.
+
+        Returns:
+            McpSessionConfig: The configuration of the adapter.
+        """
+        return McpSessionActorConfig(server_params=self.server_params)
+
+    @classmethod
+    def _from_config(cls, config: McpSessionActorConfig) -> Self:
+        """
+        Create an instance of McpSessionActor from its configuration.
+
+        Args:
+            config (McpSessionConfig): The configuration of the adapter.
+
+        Returns:
+            McpSessionActor: An instance of SseMcpToolAdapter.
+        """
+        return cls(server_params=config.server_params)

agent_dhal/agentdhal_extensions/tools/mcp/_base.py

@@ -0,0 +1,190 @@
+import asyncio
+import builtins
+import json
+from abc import ABC
+from typing import Any, Dict, Generic, Sequence, Type, TypeVar
+
+from agentdhal_core import CancellationToken
+from agentdhal_core.tools import BaseTool
+from agentdhal_core.utils import schema_to_pydantic_model
+from pydantic import BaseModel
+from pydantic.networks import AnyUrl
+
+from mcp import ClientSession, Tool
+from mcp.types import AudioContent, ContentBlock, EmbeddedResource, ImageContent, ResourceLink, TextContent
+
+from ._config import McpServerParams
+from ._session import create_mcp_server_session
+
+TServerParams = TypeVar("TServerParams", bound=McpServerParams)
+
+
+class McpToolAdapter(BaseTool[BaseModel, Any], ABC, Generic[TServerParams]):
+    """
+    Base adapter class for MCP tools to make them compatible with AutoGen.
+
+    Args:
+        server_params (TServerParams): Parameters for the MCP server connection.
+        tool (Tool): The MCP tool to wrap.
+    """
+
+    component_type = "tool"
+
+    def __init__(self, server_params: TServerParams, tool: Tool, session: ClientSession | None = None) -> None:
+        self._tool = tool
+        self._server_params = server_params
+        self._session = session
+
+        # Extract name and description
+        name = tool.name
+        description = tool.description or ""
+
+        # Create the input model from the tool's schema
+        input_model = schema_to_pydantic_model(tool.inputSchema)
+
+        # Use Any as return type since MCP tool returns can vary
+        return_type: Type[Any] = object
+
+        super().__init__(input_model, return_type, name, description)
+
+    async def run(self, args: BaseModel, cancellation_token: CancellationToken) -> Any:
+        """
+        Run the MCP tool with the provided arguments.
+
+        Args:
+            args (BaseModel): The arguments to pass to the tool.
+            cancellation_token (CancellationToken): Token to signal cancellation.
+
+        Returns:
+            Any: The result of the tool execution.
+
+        Raises:
+            Exception: If the operation is cancelled or the tool execution fails.
+        """
+        # Convert the input model to a dictionary
+        # Exclude unset values to avoid sending them to the MCP servers which may cause errors
+        # for many servers.
+        kwargs = args.model_dump(exclude_unset=True)
+
+        if self._session is not None:
+            # If a session is provided, use it directly.
+            session = self._session
+            return await self._run(args=kwargs, cancellation_token=cancellation_token, session=session)
+
+        async with create_mcp_server_session(self._server_params) as session:
+            await session.initialize()
+            return await self._run(args=kwargs, cancellation_token=cancellation_token, session=session)
+
+    def _normalize_payload_to_content_list(self, payload: Sequence[ContentBlock]) -> list[ContentBlock]:
+        """
+        Normalizes a raw tool output payload into a list of content items.
+        - If payload is already a sequence of ContentBlock items, it's converted to a list and returned.
+        - If payload is a single ContentBlock item, it's wrapped in a list.
+        - If payload is a string, it's wrapped in [TextContent(text=payload)].
+        - Otherwise, the payload is stringified and wrapped in [TextContent(text=str(payload))].
+        """
+        if isinstance(payload, Sequence) and all(
+            isinstance(item, (TextContent, ImageContent, EmbeddedResource, AudioContent, ResourceLink))
+            for item in payload
+        ):
+            return list(payload)
+        elif isinstance(payload, (TextContent, ImageContent, EmbeddedResource, AudioContent, ResourceLink)):
+            return [payload]
+        elif isinstance(payload, str):
+            return [TextContent(text=payload, type="text")]
+        else:
+            return [TextContent(text=str(payload), type="text")]
+
+    async def _run(self, args: Dict[str, Any], cancellation_token: CancellationToken, session: ClientSession) -> Any:
+        exceptions_to_catch: tuple[Type[BaseException], ...]
+        if hasattr(builtins, "ExceptionGroup"):
+            exceptions_to_catch = (asyncio.CancelledError, builtins.ExceptionGroup)
+        else:
+            exceptions_to_catch = (asyncio.CancelledError,)
+
+        try:
+            if cancellation_token.is_cancelled():
+                raise asyncio.CancelledError("Operation cancelled")
+
+            result_future = asyncio.ensure_future(session.call_tool(name=self._tool.name, arguments=args))
+            cancellation_token.link_future(result_future)
+            result = await result_future
+
+            normalized_content_list = self._normalize_payload_to_content_list(result.content)
+
+            if result.isError:
+                serialized_error_message = self.return_value_as_string(normalized_content_list)
+                raise Exception(serialized_error_message)
+            return normalized_content_list
+
+        except exceptions_to_catch:
+            # Re-raise these specific exception types directly.
+            raise
+
+    @classmethod
+    async def from_server_params(cls, server_params: TServerParams, tool_name: str) -> "McpToolAdapter[TServerParams]":
+        """
+        Create an instance of McpToolAdapter from server parameters and tool name.
+
+        Args:
+            server_params (TServerParams): Parameters for the MCP server connection.
+            tool_name (str): The name of the tool to wrap.
+
+        Returns:
+            McpToolAdapter[TServerParams]: An instance of McpToolAdapter.
+
+        Raises:
+            ValueError: If the tool with the specified name is not found.
+        """
+        async with create_mcp_server_session(server_params) as session:
+            await session.initialize()
+
+            tools_response = await session.list_tools()
+            matching_tool = next((t for t in tools_response.tools if t.name == tool_name), None)
+
+            if matching_tool is None:
+                raise ValueError(
+                    f"Tool '{tool_name}' not found, available tools: {', '.join([t.name for t in tools_response.tools])}"
+                )
+
+        return cls(server_params=server_params, tool=matching_tool)
+
+    def return_value_as_string(self, value: list[Any]) -> str:
+        """Return a string representation of the result."""
+
+        def serialize_item(item: Any) -> dict[str, Any]:
+            if isinstance(item, (TextContent, ImageContent, AudioContent)):
+                dumped = item.model_dump()
+                # Remove the 'meta' field if it exists and is None (for backward compatibility)
+                if dumped.get("meta") is None:
+                    dumped.pop("meta", None)
+                return dumped
+            elif isinstance(item, EmbeddedResource):
+                type = item.type
+                resource = {}
+                for key, val in item.resource.model_dump().items():
+                    # Skip 'meta' field if it's None (for backward compatibility)
+                    if key == "meta" and val is None:
+                        continue
+                    if isinstance(val, AnyUrl):
+                        resource[key] = str(val)
+                    else:
+                        resource[key] = val
+                dumped_annotations = item.annotations.model_dump() if item.annotations else None
+                # Remove 'meta' from annotations if it exists and is None
+                if dumped_annotations and dumped_annotations.get("meta") is None:
+                    dumped_annotations.pop("meta", None)
+                return {"type": type, "resource": resource, "annotations": dumped_annotations}
+            elif isinstance(item, ResourceLink):
+                dumped = item.model_dump()
+                # Remove the 'meta' field if it exists and is None (for backward compatibility)
+                if dumped.get("meta") is None:
+                    dumped.pop("meta", None)
+                # Convert AnyUrl to string for JSON serialization
+                if "uri" in dumped and isinstance(dumped["uri"], AnyUrl):
+                    dumped["uri"] = str(dumped["uri"])
+                return dumped
+            else:
+                return {}
+
+        return json.dumps([serialize_item(item) for item in value])

agent_dhal/agentdhal_extensions/tools/mcp/_config.py

@@ -0,0 +1,42 @@
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+from typing_extensions import Annotated
+
+from mcp import StdioServerParameters
+
+
+class StdioServerParams(StdioServerParameters):
+    """Parameters for connecting to an MCP server over STDIO."""
+
+    type: Literal["StdioServerParams"] = "StdioServerParams"
+
+    read_timeout_seconds: float = 5
+
+
+class SseServerParams(BaseModel):
+    """Parameters for connecting to an MCP server over SSE."""
+
+    type: Literal["SseServerParams"] = "SseServerParams"
+
+    url: str  # The SSE endpoint URL.
+    headers: dict[str, Any] | None = None  # Optional headers to include in requests.
+    timeout: float = 5  # HTTP timeout for regular operations.
+    sse_read_timeout: float = 60 * 5  # Timeout for SSE read operations.
+
+
+class StreamableHttpServerParams(BaseModel):
+    """Parameters for connecting to an MCP server over Streamable HTTP."""
+
+    type: Literal["StreamableHttpServerParams"] = "StreamableHttpServerParams"
+
+    url: str  # The endpoint URL.
+    headers: dict[str, Any] | None = None  # Optional headers to include in requests.
+    timeout: float = 30.0  # HTTP timeout for regular operations in seconds.
+    sse_read_timeout: float = 300.0  # Timeout for SSE read operations in seconds.
+    terminate_on_close: bool = True
+
+
+McpServerParams = Annotated[
+    StdioServerParams | SseServerParams | StreamableHttpServerParams, Field(discriminator="type")
+]

agent_dhal/agentdhal_extensions/tools/mcp/_factory.py

@@ -0,0 +1,214 @@
+from mcp import ClientSession
+
+from ._config import McpServerParams, SseServerParams, StdioServerParams, StreamableHttpServerParams
+from ._session import create_mcp_server_session
+from ._sse import SseMcpToolAdapter
+from ._stdio import StdioMcpToolAdapter
+from ._streamable_http import StreamableHttpMcpToolAdapter
+
+
+async def mcp_server_tools(
+    server_params: McpServerParams,
+    session: ClientSession | None = None,
+) -> list[StdioMcpToolAdapter | SseMcpToolAdapter | StreamableHttpMcpToolAdapter]:
+    """Creates a list of MCP tool adapters that can be used with AutoGen agents.
+
+    .. warning::
+
+        Only connect to trusted MCP servers, especially when using
+        `StdioServerParams` as it executes commands in the local environment.
+
+    This factory function connects to an MCP server and returns adapters for all available tools.
+    The adapters can be directly assigned to an AutoGen agent's tools list.
+
+    .. note::
+
+        To use this function, you need to install `mcp` extra for the `autogen-ext` package.
+
+        .. code-block:: bash
+
+            pip install -U "agentdhal-ext[mcp]"
+
+    Args:
+        server_params (McpServerParams): Connection parameters for the MCP server.
+            Can be either StdioServerParams for command-line tools or
+            SseServerParams and StreamableHttpServerParams for HTTP/SSE services.
+        session (ClientSession | None): Optional existing session to use. This is used
+            when you want to reuse an existing connection to the MCP server. The session
+            will be reused when creating the MCP tool adapters.
+
+    Returns:
+        list[StdioMcpToolAdapter | SseMcpToolAdapter | StreamableHttpMcpToolAdapter]:
+            A list of tool adapters ready to use with AutoGen agents.
+
+    Examples:
+
+        **Local file system MCP service over standard I/O example:**
+
+        Install the filesystem server package from npm (requires Node.js 16+ and npm).
+
+        .. code-block:: bash
+
+            npm install -g @modelcontextprotocol/server-filesystem
+
+        Create an agent that can use all tools from the local filesystem MCP server.
+
+        .. code-block:: python
+
+            import asyncio
+            from pathlib import Path
+            from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
+            from agentdhal_extensions.tools.mcp import StdioServerParams, mcp_server_tools
+            from agentdhal_agentchat.agents import AssistantAgent
+            from agentdhal_core import CancellationToken
+
+
+            async def main() -> None:
+                # Setup server params for local filesystem access
+                desktop = str(Path.home() / "Desktop")
+                server_params = StdioServerParams(
+                    command="npx.cmd", args=["-y", "@modelcontextprotocol/server-filesystem", desktop]
+                )
+
+                # Get all available tools from the server
+                tools = await mcp_server_tools(server_params)
+
+                # Create an agent that can use all the tools
+                agent = AssistantAgent(
+                    name="file_manager",
+                    model_client=OpenAIChatCompletionClient(model="gpt-4"),
+                    tools=tools,  # type: ignore
+                )
+
+                # The agent can now use any of the filesystem tools
+                await agent.run(task="Create a file called test.txt with some content", cancellation_token=CancellationToken())
+
+
+            if __name__ == "__main__":
+                asyncio.run(main())
+
+        **Local fetch MCP service over standard I/O example:**
+
+        Install the `mcp-server-fetch` package.
+
+        .. code-block:: bash
+
+            pip install mcp-server-fetch
+
+        Create an agent that can use the `fetch` tool from the local MCP server.
+
+        .. code-block:: python
+
+            import asyncio
+
+            from agentdhal_agentchat.agents import AssistantAgent
+            from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
+            from agentdhal_extensions.tools.mcp import StdioServerParams, mcp_server_tools
+
+
+            async def main() -> None:
+                # Get the fetch tool from mcp-server-fetch.
+                fetch_mcp_server = StdioServerParams(command="uvx", args=["mcp-server-fetch"])
+                tools = await mcp_server_tools(fetch_mcp_server)
+
+                # Create an agent that can use the fetch tool.
+                model_client = OpenAIChatCompletionClient(model="gpt-4o")
+                agent = AssistantAgent(name="fetcher", model_client=model_client, tools=tools, reflect_on_tool_use=True)  # type: ignore
+
+                # Let the agent fetch the content of a URL and summarize it.
+                result = await agent.run(task="Summarize the content of https://en.wikipedia.org/wiki/Seattle")
+                print(result.messages[-1])
+
+
+            asyncio.run(main())
+
+        **Sharing an MCP client session across multiple tools:**
+
+        You can create a single MCP client session and share it across multiple tools.
+        This is sometimes required when the server maintains a session state
+        (e.g., a browser state) that should be reused for multiple requests.
+
+        The following example show how to create a single MCP client session
+        to a local `Playwright <https://github.com/microsoft/playwright-mcp>`_
+        server and share it across multiple tools.
+
+
+        .. code-block:: python
+
+            import asyncio
+
+            from agentdhal_agentchat.agents import AssistantAgent
+            from agentdhal_agentchat.conditions import TextMentionTermination
+            from agentdhal_agentchat.teams import RoundRobinGroupChat
+            from agentdhal_agentchat.ui import Console
+            from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
+            from agentdhal_extensions.tools.mcp import StdioServerParams, create_mcp_server_session, mcp_server_tools
+
+
+            async def main() -> None:
+                model_client = OpenAIChatCompletionClient(model="gpt-4o", parallel_tool_calls=False)  # type: ignore
+                params = StdioServerParams(
+                    command="npx",
+                    args=["@playwright/mcp@latest"],
+                    read_timeout_seconds=60,
+                )
+                async with create_mcp_server_session(params) as session:
+                    await session.initialize()
+                    tools = await mcp_server_tools(server_params=params, session=session)
+                    print(f"Tools: {[tool.name for tool in tools]}")
+
+                    agent = AssistantAgent(
+                        name="Assistant",
+                        model_client=model_client,
+                        tools=tools,  # type: ignore
+                    )
+
+                    termination = TextMentionTermination("TERMINATE")
+                    team = RoundRobinGroupChat([agent], termination_condition=termination)
+                    await Console(
+                        team.run_stream(
+                            task="Go to https://ekzhu.com/, visit the first link in the page, then tell me about the linked page."
+                        )
+                    )
+
+
+            asyncio.run(main())
+
+
+        **Remote MCP service over SSE example:**
+
+        .. code-block:: python
+
+            from agentdhal_extensions.tools.mcp import SseServerParams, mcp_server_tools
+
+
+            async def main() -> None:
+                # Setup server params for remote service
+                server_params = SseServerParams(url="https://api.example.com/mcp", headers={"Authorization": "Bearer token"})
+
+                # Get all available tools
+                tools = await mcp_server_tools(server_params)
+
+                # Create an agent with all tools
+                agent = AssistantAgent(name="tool_user", model_client=OpenAIChatCompletionClient(model="gpt-4"), tools=tools)  # type: ignore
+
+    For more examples and detailed usage, see the samples directory in the package repository.
+    """
+    if session is None:
+        async with create_mcp_server_session(server_params) as temp_session:
+            await temp_session.initialize()
+
+            tools = await temp_session.list_tools()
+    else:
+        tools = await session.list_tools()
+
+    if isinstance(server_params, StdioServerParams):
+        return [StdioMcpToolAdapter(server_params=server_params, tool=tool, session=session) for tool in tools.tools]
+    elif isinstance(server_params, SseServerParams):
+        return [SseMcpToolAdapter(server_params=server_params, tool=tool, session=session) for tool in tools.tools]
+    elif isinstance(server_params, StreamableHttpServerParams):
+        return [
+            StreamableHttpMcpToolAdapter(server_params=server_params, tool=tool, session=session)
+            for tool in tools.tools
+        ]
+    raise ValueError(f"Unsupported server params type: {type(server_params)}")

agent_dhal/agentdhal_extensions/tools/mcp/_session.py

@@ -0,0 +1,55 @@
+from contextlib import asynccontextmanager
+from datetime import timedelta
+from typing import AsyncGenerator
+
+from mcp import ClientSession
+from mcp.client.session import SamplingFnT
+from mcp.client.sse import sse_client
+from mcp.client.stdio import stdio_client
+from mcp.client.streamable_http import streamablehttp_client
+
+from ._config import McpServerParams, SseServerParams, StdioServerParams, StreamableHttpServerParams
+
+
+@asynccontextmanager
+async def create_mcp_server_session(
+    server_params: McpServerParams, sampling_callback: SamplingFnT | None = None
+) -> AsyncGenerator[ClientSession, None]:
+    """Create an MCP client session for the given server parameters."""
+    if isinstance(server_params, StdioServerParams):
+        async with stdio_client(server_params) as (read, write):
+            async with ClientSession(
+                read_stream=read,
+                write_stream=write,
+                read_timeout_seconds=timedelta(seconds=server_params.read_timeout_seconds),
+                sampling_callback=sampling_callback,
+            ) as session:
+                yield session
+    elif isinstance(server_params, SseServerParams):
+        async with sse_client(**server_params.model_dump(exclude={"type"})) as (read, write):
+            async with ClientSession(
+                read_stream=read,
+                write_stream=write,
+                read_timeout_seconds=timedelta(seconds=server_params.sse_read_timeout),
+                sampling_callback=sampling_callback,
+            ) as session:
+                yield session
+    elif isinstance(server_params, StreamableHttpServerParams):
+        # Convert float seconds to timedelta for the streamablehttp_client
+        params_dict = server_params.model_dump(exclude={"type"})
+        params_dict["timeout"] = timedelta(seconds=server_params.timeout)
+        params_dict["sse_read_timeout"] = timedelta(seconds=server_params.sse_read_timeout)
+
+        async with streamablehttp_client(**params_dict) as (
+            read,
+            write,
+            session_id_callback,  # type: ignore[assignment, unused-variable]
+        ):
+            # TODO: Handle session_id_callback if needed
+            async with ClientSession(
+                read_stream=read,
+                write_stream=write,
+                read_timeout_seconds=timedelta(seconds=server_params.sse_read_timeout),
+                sampling_callback=sampling_callback,
+            ) as session:
+                yield session

agent_dhal/agentdhal_extensions/tools/mcp/_sse.py

@@ -0,0 +1,116 @@
+from agentdhal_core import Component
+from pydantic import BaseModel
+from typing_extensions import Self
+
+from mcp import ClientSession, Tool
+
+from ._base import McpToolAdapter
+from ._config import SseServerParams
+
+
+class SseMcpToolAdapterConfig(BaseModel):
+    """Configuration for the MCP tool adapter."""
+
+    server_params: SseServerParams
+    tool: Tool
+
+
+class SseMcpToolAdapter(
+    McpToolAdapter[SseServerParams],
+    Component[SseMcpToolAdapterConfig],
+):
+    """
+    Allows you to wrap an MCP tool running over Server-Sent Events (SSE) and make it available to AutoGen.
+
+    This adapter enables using MCP-compatible tools that communicate over HTTP with SSE
+    with AutoGen agents. Common use cases include integrating with remote MCP services,
+    cloud-based tools, and web APIs that implement the Model Context Protocol (MCP).
+
+    .. note::
+
+        To use this class, you need to install `mcp` extra for the `autogen-ext` package.
+
+        .. code-block:: bash
+
+            pip install -U "agentdhal-ext[mcp]"
+
+    Args:
+        server_params (SseServerParameters): Parameters for the MCP server connection,
+            including URL, headers, and timeouts.
+        tool (Tool): The MCP tool to wrap.
+        session (ClientSession, optional): The MCP client session to use. If not provided,
+            it will create a new session. This is useful for testing or when you want to
+            manage the session lifecycle yourself.
+
+    Examples:
+        Use a remote translation service that implements MCP over SSE to create tools
+        that allow AutoGen agents to perform translations:
+
+        .. code-block:: python
+
+            import asyncio
+            from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
+            from agentdhal_extensions.tools.mcp import SseMcpToolAdapter, SseServerParams
+            from agentdhal_agentchat.agents import AssistantAgent
+            from agentdhal_agentchat.ui import Console
+            from agentdhal_core import CancellationToken
+
+
+            async def main() -> None:
+                # Create server params for the remote MCP service
+                server_params = SseServerParams(
+                    url="https://api.example.com/mcp",
+                    headers={"Authorization": "Bearer your-api-key", "Content-Type": "application/json"},
+                    timeout=30,  # Connection timeout in seconds
+                )
+
+                # Get the translation tool from the server
+                adapter = await SseMcpToolAdapter.from_server_params(server_params, "translate")
+
+                # Create an agent that can use the translation tool
+                model_client = OpenAIChatCompletionClient(model="gpt-4")
+                agent = AssistantAgent(
+                    name="translator",
+                    model_client=model_client,
+                    tools=[adapter],
+                    system_message="You are a helpful translation assistant.",
+                )
+
+                # Let the agent translate some text
+                await Console(
+                    agent.run_stream(task="Translate 'Hello, how are you?' to Spanish", cancellation_token=CancellationToken())
+                )
+
+
+            if __name__ == "__main__":
+                asyncio.run(main())
+
+    """
+
+    component_config_schema = SseMcpToolAdapterConfig
+    component_provider_override = "agentdhal_extensions.tools.mcp.SseMcpToolAdapter"
+
+    def __init__(self, server_params: SseServerParams, tool: Tool, session: ClientSession | None = None) -> None:
+        super().__init__(server_params=server_params, tool=tool, session=session)
+
+    def _to_config(self) -> SseMcpToolAdapterConfig:
+        """
+        Convert the adapter to its configuration representation.
+
+        Returns:
+            SseMcpToolAdapterConfig: The configuration of the adapter.
+        """
+        return SseMcpToolAdapterConfig(server_params=self._server_params, tool=self._tool)
+
+    @classmethod
+    def _from_config(cls, config: SseMcpToolAdapterConfig) -> Self:
+        """
+        Create an instance of SseMcpToolAdapter from its configuration.
+
+        Args:
+            config (SseMcpToolAdapterConfig): The configuration of the adapter.
+
+        Returns:
+            SseMcpToolAdapter: An instance of SseMcpToolAdapter.
+        """
+        return cls(server_params=config.server_params, tool=config.tool)

agent_dhal/agentdhal_extensions/tools/mcp/_stdio.py

@@ -0,0 +1,74 @@
+from agentdhal_core import Component
+from pydantic import BaseModel
+from typing_extensions import Self
+
+from mcp import ClientSession, Tool
+
+from ._base import McpToolAdapter
+from ._config import StdioServerParams
+
+
+class StdioMcpToolAdapterConfig(BaseModel):
+    """Configuration for the MCP tool adapter."""
+
+    server_params: StdioServerParams
+    tool: Tool
+
+
+class StdioMcpToolAdapter(
+    McpToolAdapter[StdioServerParams],
+    Component[StdioMcpToolAdapterConfig],
+):
+    """Allows you to wrap an MCP tool running over STDIO and make it available to AutoGen.
+
+    This adapter enables using MCP-compatible tools that communicate over standard input/output
+    with AutoGen agents. Common use cases include wrapping command-line tools and local services
+    that implement the Model Context Protocol (MCP).
+
+    .. note::
+
+        To use this class, you need to install `mcp` extra for the `autogen-ext` package.
+
+        .. code-block:: bash
+
+            pip install -U "agentdhal-ext[mcp]"
+
+
+    Args:
+        server_params (StdioServerParams): Parameters for the MCP server connection,
+            including command to run and its arguments
+        tool (Tool): The MCP tool to wrap
+        session (ClientSession, optional): The MCP client session to use. If not provided,
+            a new session will be created. This is useful for testing or when you want to
+            manage the session lifecycle yourself.
+
+    See :func:`~agentdhal_extensions.tools.mcp.mcp_server_tools` for examples.
+    """
+
+    component_config_schema = StdioMcpToolAdapterConfig
+    component_provider_override = "agentdhal_extensions.tools.mcp.StdioMcpToolAdapter"
+
+    def __init__(self, server_params: StdioServerParams, tool: Tool, session: ClientSession | None = None) -> None:
+        super().__init__(server_params=server_params, tool=tool, session=session)
+
+    def _to_config(self) -> StdioMcpToolAdapterConfig:
+        """
+        Convert the adapter to its configuration representation.
+
+        Returns:
+            StdioMcpToolAdapterConfig: The configuration of the adapter.
+        """
+        return StdioMcpToolAdapterConfig(server_params=self._server_params, tool=self._tool)
+
+    @classmethod
+    def _from_config(cls, config: StdioMcpToolAdapterConfig) -> Self:
+        """
+        Create an instance of StdioMcpToolAdapter from its configuration.
+
+        Args:
+            config (StdioMcpToolAdapterConfig): The configuration of the adapter.
+
+        Returns:
+            StdioMcpToolAdapter: An instance of StdioMcpToolAdapter.
+        """
+        return cls(server_params=config.server_params, tool=config.tool)

agent_dhal/agentdhal_extensions/tools/mcp/_streamable_http.py

@@ -0,0 +1,121 @@
+from agentdhal_core import Component
+from pydantic import BaseModel
+from typing_extensions import Self
+
+from mcp import ClientSession, Tool
+
+from ._base import McpToolAdapter
+from ._config import StreamableHttpServerParams
+
+
+class StreamableHttpMcpToolAdapterConfig(BaseModel):
+    """Configuration for the MCP tool adapter."""
+
+    server_params: StreamableHttpServerParams
+    tool: Tool
+
+
+class StreamableHttpMcpToolAdapter(
+    McpToolAdapter[StreamableHttpServerParams],
+    Component[StreamableHttpMcpToolAdapterConfig],
+):
+    """
+    Allows you to wrap an MCP tool running over Streamable HTTP and make it available to AutoGen.
+
+    This adapter enables using MCP-compatible tools that communicate over Streamable HTTP
+    with AutoGen agents. Common use cases include integrating with remote MCP services,
+    cloud-based tools, and web APIs that implement the Model Context Protocol (MCP).
+
+    .. note::
+
+        To use this class, you need to install `mcp` extra for the `autogen-ext` package.
+
+        .. code-block:: bash
+
+            pip install -U "agentdhal-ext[mcp]"
+
+
+    Args:
+        server_params (StreamableHttpServerParams): Parameters for the MCP server connection,
+            including URL, headers, and timeouts.
+        tool (Tool): The MCP tool to wrap.
+        session (ClientSession, optional): The MCP client session to use. If not provided,
+            it will create a new session. This is useful for testing or when you want to
+            manage the session lifecycle yourself.
+
+    Examples:
+        Use a remote translation service that implements MCP over Streamable HTTP to
+        create tools that allow AutoGen agents to perform translations:
+
+        .. code-block:: python
+
+            import asyncio
+            from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
+            from agentdhal_extensions.tools.mcp import StreamableHttpMcpToolAdapter, StreamableHttpServerParams
+            from agentdhal_agentchat.agents import AssistantAgent
+            from agentdhal_agentchat.ui import Console
+            from agentdhal_core import CancellationToken
+
+
+            async def main() -> None:
+                # Create server params for the remote MCP service
+                server_params = StreamableHttpServerParams(
+                    url="https://api.example.com/mcp",
+                    headers={"Authorization": "Bearer your-api-key", "Content-Type": "application/json"},
+                    timeout=30.0,  # HTTP timeout in seconds
+                    sse_read_timeout=300.0,  # SSE read timeout in seconds (5 minutes)
+                    terminate_on_close=True,
+                )
+
+                # Get the translation tool from the server
+                adapter = await StreamableHttpMcpToolAdapter.from_server_params(server_params, "translate")
+
+                # Create an agent that can use the translation tool
+                model_client = OpenAIChatCompletionClient(model="gpt-4")
+                agent = AssistantAgent(
+                    name="translator",
+                    model_client=model_client,
+                    tools=[adapter],
+                    system_message="You are a helpful translation assistant.",
+                )
+
+                # Let the agent translate some text
+                await Console(
+                    agent.run_stream(task="Translate 'Hello, how are you?' to Spanish", cancellation_token=CancellationToken())
+                )
+
+
+            if __name__ == "__main__":
+                asyncio.run(main())
+
+    """
+
+    component_config_schema = StreamableHttpMcpToolAdapterConfig
+    component_provider_override = "agentdhal_extensions.tools.mcp.StreamableHttpMcpToolAdapter"
+
+    def __init__(
+        self, server_params: StreamableHttpServerParams, tool: Tool, session: ClientSession | None = None
+    ) -> None:
+        super().__init__(server_params=server_params, tool=tool, session=session)
+
+    def _to_config(self) -> StreamableHttpMcpToolAdapterConfig:
+        """
+        Convert the adapter to its configuration representation.
+
+        Returns:
+            StreamableHttpMcpToolAdapterConfig: The configuration of the adapter.
+        """
+        return StreamableHttpMcpToolAdapterConfig(server_params=self._server_params, tool=self._tool)
+
+    @classmethod
+    def _from_config(cls, config: StreamableHttpMcpToolAdapterConfig) -> Self:
+        """
+        Create an instance of StreamableHttpMcpToolAdapter from its configuration.
+
+        Args:
+            config (StreamableHttpMcpToolAdapterConfig): The configuration of the adapter.
+
+        Returns:
+            StreamableHttpMcpToolAdapter: An instance of StreamableHttpMcpToolAdapter.
+        """
+        return cls(server_params=config.server_params, tool=config.tool)

agent_dhal/agentdhal_extensions/tools/mcp/_workbench.py

@@ -0,0 +1,518 @@
+import asyncio
+import builtins
+import warnings
+from typing import Any, Dict, List, Literal, Mapping, Optional
+
+from agentdhal_core import CancellationToken, Component, ComponentModel, Image, trace_tool_span
+from agentdhal_core.models import ChatCompletionClient
+from agentdhal_core.tools import (
+    ImageResultContent,
+    ParametersSchema,
+    TextResultContent,
+    ToolOverride,
+    ToolResult,
+    ToolSchema,
+    Workbench,
+)
+from pydantic import BaseModel, Field
+from typing_extensions import Self
+
+from mcp.types import (
+    CallToolResult,
+    EmbeddedResource,
+    GetPromptResult,
+    ImageContent,
+    ListPromptsResult,
+    ListResourcesResult,
+    ListResourceTemplatesResult,
+    ListToolsResult,
+    ReadResourceResult,
+    TextContent,
+)
+
+from ._actor import McpSessionActor
+from ._config import McpServerParams, SseServerParams, StdioServerParams, StreamableHttpServerParams
+
+
+class McpWorkbenchConfig(BaseModel):
+    server_params: McpServerParams
+    tool_overrides: Dict[str, ToolOverride] = Field(default_factory=dict)
+    model_client: ComponentModel | Dict[str, Any] | None = None
+
+
+class McpWorkbenchState(BaseModel):
+    type: Literal["McpWorkBenchState"] = "McpWorkBenchState"
+
+
+class McpWorkbench(Workbench, Component[McpWorkbenchConfig]):
+    """A workbench that wraps an MCP server and provides an interface
+    to list and call tools provided by the server.
+
+    .. warning::
+
+        Only connect to trusted MCP servers, especially when using
+        `StdioServerParams` as it executes commands in the local environment.
+
+    This workbench should be used as a context manager to ensure proper
+    initialization and cleanup of the underlying MCP session.
+
+    .. list-table:: MCP Support
+       :header-rows: 1
+       :widths: 30 70
+
+       * - MCP Capability
+         - Supported Features
+       * - Tools
+         - list_tools, call_tool
+       * - Resources
+         - list_resources, read_resource
+       * - ResourceTemplates
+         - list_resource_templates, read_resource_template
+       * - Prompts
+         - list_prompts, get_prompt
+       * - Sampling
+         - Optional support via model_client
+       * - Roots
+         - not supported
+       * - Ellicitation
+         - not supported
+
+    Args:
+        server_params (McpServerParams): The parameters to connect to the MCP server.
+            This can be either a :class:`StdioServerParams` or :class:`SseServerParams`.
+        tool_overrides (Optional[Dict[str, ToolOverride]]): Optional mapping of original tool
+            names to override configurations for name and/or description. This allows
+            customizing how server tools appear to consumers while maintaining the underlying
+            tool functionality.
+        model_client: Optional chat completion client to handle sampling requests
+            from MCP servers that support the sampling capability. This allows MCP
+            servers to request text generation from a language model during tool
+            execution. If not provided, sampling requests will return an error.
+
+    Raises:
+        ValueError: If there are conflicts in tool override names.
+
+    Examples:
+
+        Here is a simple example of how to use the workbench with a `mcp-server-fetch` server:
+
+        .. code-block:: python
+
+            import asyncio
+
+            from agentdhal_extensions.tools.mcp import McpWorkbench, StdioServerParams
+
+
+            async def main() -> None:
+                params = StdioServerParams(
+                    command="uvx",
+                    args=["mcp-server-fetch"],
+                    read_timeout_seconds=60,
+                )
+
+                # You can also use `start()` and `stop()` to manage the session.
+                async with McpWorkbench(server_params=params) as workbench:
+                    tools = await workbench.list_tools()
+                    print(tools)
+                    result = await workbench.call_tool(tools[0]["name"], {"url": "https://github.com/"})
+                    print(result)
+
+
+            asyncio.run(main())
+
+        Example of using tool overrides:
+
+        .. code-block:: python
+
+            import asyncio
+            from agentdhal_extensions.tools.mcp import McpWorkbench, StdioServerParams
+            from agentdhal_core.tools import ToolOverride
+
+
+            async def main() -> None:
+                params = StdioServerParams(
+                    command="uvx",
+                    args=["mcp-server-fetch"],
+                    read_timeout_seconds=60,
+                )
+
+                # Override the fetch tool's name and description
+                overrides = {
+                    "fetch": ToolOverride(name="web_fetch", description="Enhanced web fetching tool with better error handling")
+                }
+
+                async with McpWorkbench(server_params=params, tool_overrides=overrides) as workbench:
+                    tools = await workbench.list_tools()
+                    # The tool will now appear as "web_fetch" with the new description
+                    print(tools)
+                    # Call the overridden tool
+                    result = await workbench.call_tool("web_fetch", {"url": "https://github.com/"})
+                    print(result)
+
+
+            asyncio.run(main())
+
+        Example of using the workbench with the `GitHub MCP Server <https://github.com/github/github-mcp-server>`_:
+
+        .. code-block:: python
+
+            import asyncio
+            from agentdhal_agentchat.agents import AssistantAgent
+            from agentdhal_agentchat.ui import Console
+            from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
+            from agentdhal_extensions.tools.mcp import McpWorkbench, StdioServerParams
+
+
+            async def main() -> None:
+                model_client = OpenAIChatCompletionClient(model="gpt-4.1-nano")
+                server_params = StdioServerParams(
+                    command="docker",
+                    args=[
+                        "run",
+                        "-i",
+                        "--rm",
+                        "-e",
+                        "GITHUB_PERSONAL_ACCESS_TOKEN",
+                        "ghcr.io/github/github-mcp-server",
+                    ],
+                    env={
+                        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
+                    },
+                )
+                async with McpWorkbench(server_params) as mcp:
+                    agent = AssistantAgent(
+                        "github_assistant",
+                        model_client=model_client,
+                        workbench=mcp,
+                        reflect_on_tool_use=True,
+                        model_client_stream=True,
+                    )
+                    await Console(agent.run_stream(task="Is there a repository named Autogen"))
+
+
+            asyncio.run(main())
+
+        Example of using the workbench with the `Playwright MCP Server <https://github.com/microsoft/playwright-mcp>`_:
+
+        .. code-block:: python
+
+            # First run `npm install -g @playwright/mcp@latest` to install the MCP server.
+            import asyncio
+            from agentdhal_agentchat.agents import AssistantAgent
+            from agentdhal_agentchat.teams import RoundRobinGroupChat
+            from agentdhal_agentchat.conditions import TextMessageTermination
+            from agentdhal_agentchat.ui import Console
+            from agentdhal_extensions.models.openai import OpenAIChatCompletionClient
+            from agentdhal_extensions.tools.mcp import McpWorkbench, StdioServerParams
+
+
+            async def main() -> None:
+                model_client = OpenAIChatCompletionClient(model="gpt-4.1-nano")
+                server_params = StdioServerParams(
+                    command="npx",
+                    args=[
+                        "@playwright/mcp@latest",
+                        "--headless",
+                    ],
+                )
+                async with McpWorkbench(server_params) as mcp:
+                    agent = AssistantAgent(
+                        "web_browsing_assistant",
+                        model_client=model_client,
+                        workbench=mcp,
+                        model_client_stream=True,
+                    )
+                    team = RoundRobinGroupChat(
+                        [agent],
+                        termination_condition=TextMessageTermination(source="web_browsing_assistant"),
+                    )
+                    await Console(team.run_stream(task="Find out how many contributors for the microsoft/autogen repository"))
+
+
+            asyncio.run(main())
+
+    """
+
+    component_provider_override = "agentdhal_extensions.tools.mcp.McpWorkbench"
+    component_config_schema = McpWorkbenchConfig
+
+    def __init__(
+        self,
+        server_params: McpServerParams,
+        tool_overrides: Optional[Dict[str, ToolOverride]] = None,
+        model_client: ChatCompletionClient | None = None,
+    ) -> None:
+        self._server_params = server_params
+        self._tool_overrides = tool_overrides or {}
+        self._model_client = model_client
+
+        # Build reverse mapping from override names to original names for call_tool
+        self._override_name_to_original: Dict[str, str] = {}
+        for original_name, override in self._tool_overrides.items():
+            override_name = override.name
+            if override_name and override_name != original_name:
+                # Check for conflicts with other override names
+                if override_name in self._override_name_to_original:
+                    existing_original = self._override_name_to_original[override_name]
+                    raise ValueError(
+                        f"Tool override name '{override_name}' is used by multiple tools: "
+                        f"'{existing_original}' and '{original_name}'. Override names must be unique."
+                    )
+                self._override_name_to_original[override_name] = original_name
+
+        # self._session: ClientSession | None = None
+        self._actor: McpSessionActor | None = None
+        self._actor_loop: asyncio.AbstractEventLoop | None = None
+        self._read = None
+        self._write = None
+
+    @property
+    def server_params(self) -> McpServerParams:
+        return self._server_params
+
+    async def list_tools(self) -> List[ToolSchema]:
+        if not self._actor:
+            await self.start()  # fallback to start the actor if not initialized instead of raising an error
+            # Why? Because when deserializing the workbench, the actor might not be initialized yet.
+            # raise RuntimeError("Actor is not initialized. Call start() first.")
+        if self._actor is None:
+            raise RuntimeError("Actor is not initialized. Please check the server connection.")
+        result_future = await self._actor.call("list_tools", None)
+        list_tool_result = await result_future
+        assert isinstance(
+            list_tool_result, ListToolsResult
+        ), f"list_tools must return a CallToolResult, instead of : {str(type(list_tool_result))}"
+        schema: List[ToolSchema] = []
+        for tool in list_tool_result.tools:
+            original_name = tool.name
+            name = original_name
+            description = tool.description or ""
+
+            # Apply overrides if they exist for this tool
+            if original_name in self._tool_overrides:
+                override = self._tool_overrides[original_name]
+                if override.name is not None:
+                    name = override.name
+                if override.description is not None:
+                    description = override.description
+
+            parameters = ParametersSchema(
+                type="object",
+                properties=tool.inputSchema.get("properties", {}),
+                required=tool.inputSchema.get("required", []),
+                additionalProperties=tool.inputSchema.get("additionalProperties", False),
+            )
+            tool_schema = ToolSchema(
+                name=name,
+                description=description,
+                parameters=parameters,
+            )
+            schema.append(tool_schema)
+        return schema
+
+    async def call_tool(
+        self,
+        name: str,
+        arguments: Mapping[str, Any] | None = None,
+        cancellation_token: CancellationToken | None = None,
+        call_id: str | None = None,
+    ) -> ToolResult:
+        if not self._actor:
+            await self.start()  # fallback to start the actor if not initialized instead of raising an error
+            # Why? Because when deserializing the workbench, the actor might not be initialized yet.
+            # raise RuntimeError("Actor is not initialized. Call start() first.")
+        if self._actor is None:
+            raise RuntimeError("Actor is not initialized. Please check the server connection.")
+        if not cancellation_token:
+            cancellation_token = CancellationToken()
+        if not arguments:
+            arguments = {}
+
+        # Check if the name is an override name and map it back to the original
+        original_name = self._override_name_to_original.get(name, name)
+
+        with trace_tool_span(
+            tool_name=name,  # Use the requested name for tracing
+            tool_call_id=call_id,
+        ):
+            try:
+                result_future = await self._actor.call("call_tool", {"name": original_name, "kargs": arguments})
+                cancellation_token.link_future(result_future)
+                result = await result_future
+                assert isinstance(
+                    result, CallToolResult
+                ), f"call_tool must return a CallToolResult, instead of : {str(type(result))}"
+                result_parts: List[TextResultContent | ImageResultContent] = []
+                is_error = result.isError
+                for content in result.content:
+                    if isinstance(content, TextContent):
+                        result_parts.append(TextResultContent(content=content.text))
+                    elif isinstance(content, ImageContent):
+                        result_parts.append(ImageResultContent(content=Image.from_base64(content.data)))
+                    elif isinstance(content, EmbeddedResource):
+                        # TODO: how to handle embedded resources?
+                        # For now we just use text representation.
+                        result_parts.append(TextResultContent(content=content.model_dump_json()))
+                    else:
+                        raise ValueError(f"Unknown content type from server: {type(content)}")
+            except Exception as e:
+                error_message = self._format_errors(e)
+                is_error = True
+                result_parts = [TextResultContent(content=error_message)]
+        return ToolResult(name=name, result=result_parts, is_error=is_error)  # Return the requested name
+
+    @property
+    def initialize_result(self) -> Any:
+        if self._actor:
+            return self._actor.initialize_result
+
+        return None
+
+    async def list_prompts(self) -> ListPromptsResult:
+        """List available prompts from the MCP server."""
+        if not self._actor:
+            await self.start()
+        if self._actor is None:
+            raise RuntimeError("Actor is not initialized. Please check the server connection.")
+
+        result_future = await self._actor.call("list_prompts", None)
+        list_prompts_result = await result_future
+        assert isinstance(
+            list_prompts_result, ListPromptsResult
+        ), f"list_prompts must return a ListPromptsResult, instead of: {str(type(list_prompts_result))}"
+
+        return list_prompts_result
+
+    async def list_resources(self) -> ListResourcesResult:
+        """List available resources from the MCP server."""
+        if not self._actor:
+            await self.start()
+        if self._actor is None:
+            raise RuntimeError("Actor is not initialized. Please check the server connection.")
+
+        result_future = await self._actor.call("list_resources", None)
+        list_resources_result = await result_future
+        assert isinstance(
+            list_resources_result, ListResourcesResult
+        ), f"list_resources must return a ListResourcesResult, instead of: {str(type(list_resources_result))}"
+
+        return list_resources_result
+
+    async def list_resource_templates(self) -> ListResourceTemplatesResult:
+        """List available resource templates from the MCP server."""
+        if not self._actor:
+            await self.start()
+        if self._actor is None:
+            raise RuntimeError("Actor is not initialized. Please check the server connection.")
+
+        result_future = await self._actor.call("list_resource_templates", None)
+        list_templates_result = await result_future
+        assert isinstance(
+            list_templates_result, ListResourceTemplatesResult
+        ), f"list_resource_templates must return a ListResourceTemplatesResult, instead of: {str(type(list_templates_result))}"
+
+        return list_templates_result
+
+    async def read_resource(self, uri: str) -> ReadResourceResult:
+        """Read a resource from the MCP server."""
+        if not self._actor:
+            await self.start()
+        if self._actor is None:
+            raise RuntimeError("Actor is not initialized. Please check the server connection.")
+
+        result_future = await self._actor.call("read_resource", {"name": None, "kargs": {"uri": uri}})
+        read_resource_result = await result_future
+        assert isinstance(
+            read_resource_result, ReadResourceResult
+        ), f"read_resource must return a ReadResourceResult, instead of: {str(type(read_resource_result))}"
+
+        return read_resource_result
+
+    async def get_prompt(self, name: str, arguments: Optional[Dict[str, str]] = None) -> GetPromptResult:
+        """Get a prompt from the MCP server."""
+        if not self._actor:
+            await self.start()
+        if self._actor is None:
+            raise RuntimeError("Actor is not initialized. Please check the server connection.")
+
+        result_future = await self._actor.call("get_prompt", {"name": name, "kargs": {"arguments": arguments}})
+        get_prompt_result = await result_future
+        assert isinstance(
+            get_prompt_result, GetPromptResult
+        ), f"get_prompt must return a GetPromptResult, instead of: {str(type(get_prompt_result))}"
+
+        return get_prompt_result
+
+    def _format_errors(self, error: Exception) -> str:
+        """Recursively format errors into a string."""
+
+        error_message = ""
+        if hasattr(builtins, "ExceptionGroup") and isinstance(error, builtins.ExceptionGroup):
+            # ExceptionGroup is available in Python 3.11+.
+            # TODO: how to make this compatible with Python 3.10?
+            for sub_exception in error.exceptions:  # type: ignore
+                error_message += self._format_errors(sub_exception)  # type: ignore
+        else:
+            error_message += f"{str(error)}\n"
+        return error_message
+
+    async def start(self) -> None:
+        if self._actor:
+            warnings.warn(
+                "McpWorkbench is already started. No need to start again.",
+                UserWarning,
+                stacklevel=2,
+            )
+            return  # Already initialized, no need to start again
+
+        if isinstance(self._server_params, (StdioServerParams, SseServerParams, StreamableHttpServerParams)):
+            self._actor = McpSessionActor(self._server_params, model_client=self._model_client)
+            await self._actor.initialize()
+            self._actor_loop = asyncio.get_event_loop()
+        else:
+            raise ValueError(f"Unsupported server params type: {type(self._server_params)}")
+
+    async def stop(self) -> None:
+        if self._actor:
+            # Close the actor
+            await self._actor.close()
+            self._actor = None
+        else:
+            raise RuntimeError("McpWorkbench is not started. Call start() first.")
+
+    async def reset(self) -> None:
+        pass
+
+    async def save_state(self) -> Mapping[str, Any]:
+        return McpWorkbenchState().model_dump()
+
+    async def load_state(self, state: Mapping[str, Any]) -> None:
+        pass
+
+    def _to_config(self) -> McpWorkbenchConfig:
+        model_client_config = None
+        if self._model_client is not None:
+            model_client_config = self._model_client.dump_component()
+        return McpWorkbenchConfig(
+            server_params=self._server_params, tool_overrides=self._tool_overrides, model_client=model_client_config
+        )
+
+    @classmethod
+    def _from_config(cls, config: McpWorkbenchConfig) -> Self:
+        model_client = None
+        if config.model_client is not None:
+            model_client = ChatCompletionClient.load_component(config.model_client)
+        return cls(server_params=config.server_params, tool_overrides=config.tool_overrides, model_client=model_client)
+
+    def __del__(self) -> None:
+        # Ensure the actor is stopped when the workbench is deleted
+        # Use getattr to safely handle cases where attributes may not be set (e.g., if __init__ failed)
+        actor = getattr(self, "_actor", None)
+        actor_loop = getattr(self, "_actor_loop", None)
+
+        if actor and actor_loop:
+            if actor_loop.is_running() and not actor_loop.is_closed():
+                actor_loop.call_soon_threadsafe(lambda: asyncio.create_task(self.stop()))
+            else:
+                msg = "Cannot safely stop actor at [McpWorkbench.__del__]: loop is closed or not running"
+                warnings.warn(msg, RuntimeWarning, stacklevel=2)