Update to match Google ADK 1.3 patterns

Makefile

@@ -6,4 +6,10 @@ lint:
 	uv run ruff check .
 
 leaderboard:
-	uv run -- streamlit run agents_mcp_usage/multi_mcp/eval_multi_mcp/merbench_ui.py
+	uv run -- streamlit run agents_mcp_usage/multi_mcp/eval_multi_mcp/merbench_ui.py
+
+adk_basic_ui:
+	uv run adk web agents_mcp_usage/basic_mcp
+
+adk_multi_ui:
+	uv run adk web agents_mcp_usage/multi_mcp

README.md

@@ -38,7 +38,10 @@ Run an Agent framework script e.g.:
 - `uv run agents_mcp_usage/basic_mcp/basic_mcp_use/oai-agent_mcp.py`
   - Requires `OPENAI_API_KEY` by default
 
-Check console or Logfire for output
+- Launch the ADK web UI for visual interaction with the agents:
+  - `make adk_basic_ui`
+
+Check console, Logfire, or the ADK web UI for output
 
 ## Project Overview
 
@@ -55,7 +58,7 @@ This project aims to teach:
 
 - **[agents_mcp_usage/basic_mcp/](agents_mcp_usage/basic_mcp/)** - Single MCP server integration examples
   - **basic_mcp_use/** - Contains basic examples of single MCP usage:
-    - `adk_mcp.py` - Example of using MCP with Google's Agent Development Kit (ADK)
+    - `adk_mcp.py` - Example of using MCP with Google's Agent Development Kit (ADK 1.3.0)
     - `langgraph_mcp.py` - Example of using MCP with LangGraph
     - `oai-agent_mcp.py` - Example of using MCP with OpenAI Agents
     - `pydantic_mcp.py` - Example of using MCP with Pydantic-AI
@@ -154,6 +157,9 @@ uv run agents_mcp_usage/basic_mcp/basic_mcp_use/oai-agent_mcp.py
 
 # Pydantic-AI example
 uv run agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp.py
+
+# Launch ADK web UI for visual interaction
+make adk_basic_ui
 ```
 
 More details on basic MCP implementation can be found in the [basic_mcp README](agents_mcp_usage/basic_mcp/README.md).
@@ -218,7 +224,7 @@ uv run agents_mcp_usage/multi_mcp/multi_mcp_use/pydantic_mcp.py
 uv run agents_mcp_usage/multi_mcp/eval_multi_mcp/evals_pydantic_mcp.py
 
 # Run multi-model benchmarking
-uv run agents_mcp_usage/multi_mcp/eval_multi_mcp/run_multi_evals.py --models "gemini-2.5-pro,gemini-2.0-flash" --runs 5 --parallel
+uv run agents_mcp_usage/multi_mcp/eval_multi_mcp/run_multi_evals.py --models "gemini-2.5-pro-preview-06-05,gemini-2.0-flash" --runs 5 --parallel
 
 # Launch the evaluation dashboard
 uv run streamlit run agents_mcp_usage/multi_mcp/eval_multi_mcp/merbench_ui.py
@@ -258,7 +264,7 @@ uv run agents_mcp_usage/multi_mcp/eval_multi_mcp/evals_pydantic_mcp.py
 
 # Multi-model parallel benchmarking
 uv run agents_mcp_usage/multi_mcp/eval_multi_mcp/run_multi_evals.py \
-  --models "gemini-2.5-pro,gemini-2.0-flash,gemini-2.5-flash-preview-04-17" \
+  --models "gemini-2.5-pro-preview-06-05,gemini-2.0-flash,gemini-2.5-flash" \
   --runs 5 \
   --parallel \
   --output-dir ./results
@@ -288,6 +294,11 @@ A key advantage highlighted is flexibility; MCP allows developers to more easily
    ```bash
    make install
    ```
+
+   To use the ADK web UI, run:
+   ```bash
+   make adk_basic_ui
+   ```
 3. Set up your environment variables in a `.env` file:
    ```
    LOGFIRE_TOKEN=your_logfire_token

agents_mcp_usage/basic_mcp/README.md

@@ -21,6 +21,9 @@ Each script demonstrates how to connect to a single local MCP server and use it
 
    # Run the Google ADK example
    uv run agents_mcp_usage/basic_mcp/basic_mcp_use/adk_mcp.py
+
+   ## Launch ADK web UI for visual interaction
+   make adk_basic_ui
    ```
 
 4. Check the console output or Logfire for results.
@@ -155,18 +158,25 @@ The sequence diagram illustrates the temporal flow of interactions between the u
 
 **File:** `adk_mcp.py`
 
-This example demonstrates how to use MCP with Google's Agent Development Kit (ADK).
+This example demonstrates how to use MCP with Google's Agent Development Kit (ADK 1.3.0).
 
 ```bash
 uv run agents_mcp_usage/basic_mcp/basic_mcp_use/adk_mcp.py
 ```
 
 Key features:
-- Uses `MCPToolset` for connecting to the MCP server
+- Uses `MCPToolset` with `StdioConnectionParams` for MCP server connection
 - Configures a Gemini model using ADK's `LlmAgent`
 - Sets up session handling and runner for agent execution
+- Implements proper resource management for MCP connections
+- Supports ADK web UI through module exports
 - Includes Logfire instrumentation for tracing
 
+To run with the ADK web UI:
+```bash
+make adk_basic_ui
+```
+
 ### LangGraph
 
 **File:** `langgraph_mcp.py`

agents_mcp_usage/basic_mcp/basic_mcp_use/__init__.py

@@ -1 +1,4 @@
 # Basic MCP usage examples package
+
+# Allow discovery of the ADK agent via the ADK web UI
+from .adk_mcp import root_agent as root_agent

agents_mcp_usage/basic_mcp/basic_mcp_use/adk_mcp.py

@@ -1,14 +1,28 @@
+"""ADK-based agent using MCP protocol for tool access.
+
+This module provides an ADK agent that can be used both in the ADK web UI
+and directly from the command line. The agent uses MCP tools to access
+external functionality.
+"""
+
 import asyncio
 import os
 import logfire
+from typing import List, Tuple, Any
 
 from dotenv import load_dotenv
 from google.adk.agents.llm_agent import LlmAgent
-from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, StdioServerParameters
+from google.adk.tools.mcp_tool.mcp_toolset import (
+    MCPToolset,
+    StdioServerParameters,
+    StdioConnectionParams,
+)
 from google.adk.runners import Runner
 from google.adk.sessions import InMemorySessionService
 from google.genai import types
 
+from agents_mcp_usage.utils import get_mcp_server_path
+
 load_dotenv()
 
 # Set API key for Google AI API from environment variable
@@ -18,62 +32,149 @@
 logfire.configure(send_to_logfire="if-token-present", service_name="adk-basic-mcp")
 logfire.instrument_mcp()
 
+# Global variable to store toolset instances for cleanup
+_ACTIVE_TOOLSETS = []
 
-async def main(query: str = "Greet Andrew and give him the current time") -> None:
-    """Runs the agent with a given query.
 
-    This function sets up the MCP server, creates an LLM agent, and runs it
-    with a specified query. It also handles the cleanup of the MCP server
-    connection.
+# Initialize the MCP tools and agent for ADK web UI
+async def init_mcp_toolset() -> Tuple[List[Any], List[MCPToolset]]:
+    """Initialize and return the MCP tools and toolsets.
 
-    Args:
-        query: The query to run the agent with.
+    Returns:
+        Tuple[List[Any], List[MCPToolset]]: A tuple of (tools, toolsets)
     """
-    # Set up MCP server connection
+    global _ACTIVE_TOOLSETS
+
+    # Use absolute path to MCP server based on project root
+    mcp_server_path = get_mcp_server_path("example_server.py")
+
     server_params = StdioServerParameters(
         command="uv",
-        args=["run", "mcp_servers/example_server.py", "stdio"],
+        args=["run", str(mcp_server_path), "stdio"],
     )
+    connection_params = StdioConnectionParams(server_params=server_params)
+    toolset = MCPToolset(connection_params=connection_params)
 
-    tools, exit_stack = await MCPToolset.from_server(connection_params=server_params)
-    print(f"Connected to MCP server. Found {len(tools)} tools.")
+    try:
+        tools = await toolset.get_tools()
+        _ACTIVE_TOOLSETS.append(toolset)
+        return tools, [toolset]
+    except Exception as e:
+        # Clean up in case of initialization error
+        try:
+            await toolset.close()
+        except Exception:
+            pass
+        raise e
+
+
+async def cleanup_toolsets():
+    """Clean up any active MCP toolset connections."""
+    global _ACTIVE_TOOLSETS
+
+    for toolset in _ACTIVE_TOOLSETS:
+        try:
+            await toolset.close()
+            print("MCP toolset connection closed.")
+        except asyncio.CancelledError:
+            print("MCP cleanup cancelled - this is normal")
+        except Exception as e:
+            print(f"Warning: Error during toolset cleanup: {e}")
+
+    _ACTIVE_TOOLSETS = []
+
+
+# Define a before_agent_callback to attach tools
+async def attach_tools_callback(callback_context):
+    """Callback to attach tools to the agent before it runs.
 
-    # Create the agent
-    root_agent = LlmAgent(
-        model="gemini-2.5-pro-preview-03-25",
-        name="mcp_adk_assistant",
-        tools=tools,
-    )
+    Args:
+        callback_context: The callback context from ADK.
 
-    # Set up session
-    session_service = InMemorySessionService()
-    session = session_service.create_session(
-        app_name="mcp_adk_app",
-        user_id="aginns",
-    )
+    Returns:
+        None: The callback doesn't modify the content.
+    """
+    await ensure_tools_attached()
+    return None
+
+
+# This is the agent that will be imported by the ADK web UI
+root_agent = LlmAgent(
+    model="gemini-2.0-flash",
+    name="mcp_adk_assistant",
+    instruction="You are an assistant that uses MCP tools to help users.",
+    before_agent_callback=attach_tools_callback,  # This ensures tools are attached
+)
 
-    # Create the runner
-    runner = Runner(
-        app_name="mcp_adk_app",
-        agent=root_agent,
-        session_service=session_service,
-    )
 
-    # Run the agent with a query
-    content = types.Content(role="user", parts=[types.Part(text=query)])
+# Flag to track if tools have been attached
+TOOLS_ATTACHED = False
 
-    print("Running agent...")
+
+# Function to dynamically attach tools to the agent
+async def ensure_tools_attached():
+    """Ensures that tools are attached to the agent before it's used."""
+    global TOOLS_ATTACHED
+
+    if not TOOLS_ATTACHED:
+        try:
+            tools, _ = await init_mcp_toolset()
+            print(f"✓ Connected to MCP server. Found {len(tools)} tools.")
+            # Update the agent's tools
+            root_agent.tools = tools
+            TOOLS_ATTACHED = True
+        except Exception as e:
+            print(f"Error attaching MCP tools: {e}")
+            # Set empty tools to avoid errors
+            root_agent.tools = []
+
+
+async def main(query: str = "Greet Andrew and give him the current time") -> None:
+    """Runs the agent with a given query.
+
+    This function sets up a runner for the agent and runs it with a specified query.
+    It also handles the cleanup of the MCP server connection.
+
+    Args:
+        query: The query to run the agent with.
+    """
     try:
+        # Ensure tools are attached to the agent
+        await ensure_tools_attached()
+
+        # Set up session with async service
+        session_service = InMemorySessionService()
+        session = await session_service.create_session(
+            app_name="mcp_adk_app",
+            user_id="aginns",
+        )
+
+        # Create the runner using the globally defined agent
+        runner = Runner(
+            app_name="mcp_adk_app",
+            agent=root_agent,
+            session_service=session_service,
+        )
+
+        # Format the query as content
+        content = types.Content(role="user", parts=[types.Part(text=query)])
+
+        print("Running agent...")
         events_async = runner.run_async(
             session_id=session.id, user_id=session.user_id, new_message=content
         )
 
         async for event in events_async:
             print(f"Event received: {event}")
+
+    except Exception as e:
+        print(f"Error during agent execution: {e}")
+        print(f"Error type: {type(e).__name__}")
+        raise
     finally:
-        print("Closing MCP server connection...")
-        await exit_stack.aclose()
-        print("Cleanup complete.")
+        # Clean up MCP toolsets to prevent asyncio shutdown errors
+        await cleanup_toolsets()
+        print("Agent execution completed successfully.")
 
 
 if __name__ == "__main__":

agents_mcp_usage/basic_mcp/basic_mcp_use/langgraph_mcp.py

@@ -9,6 +9,8 @@
 from mcp import ClientSession, StdioServerParameters
 from mcp.client.stdio import stdio_client
 
+from agents_mcp_usage.utils import get_mcp_server_path
+
 load_dotenv()
 
 # Configure logging if LOGFIRE_TOKEN is set
@@ -21,11 +23,11 @@
 # Create server parameters for stdio connection
 server = StdioServerParameters(
     command="uv",
-    args=["run", "mcp_servers/example_server.py", "stdio"],
+    args=["run", str(get_mcp_server_path("example_server.py")), "stdio"],
 )
 
 model = ChatGoogleGenerativeAI(
-    model="gemini-2.5-pro-preview-03-25", google_api_key=os.getenv("GEMINI_API_KEY")
+    model="gemini-2.5-pro-preview-06-05", google_api_key=os.getenv("GEMINI_API_KEY")
 )
 
 

agents_mcp_usage/basic_mcp/basic_mcp_use/oai-agent_mcp.py

@@ -5,6 +5,8 @@
 from agents.mcp import MCPServerStdio
 from dotenv import load_dotenv
 
+from agents_mcp_usage.utils import get_mcp_server_path
+
 load_dotenv()
 
 # Configure Logfire
@@ -26,7 +28,7 @@ async def main(query: str = "Greet Andrew and give him the current time") -> Non
     async with MCPServerStdio(
         params={
             "command": "uv",
-            "args": ["run", "mcp_servers/example_server.py", "stdio"],
+            "args": ["run", str(get_mcp_server_path("example_server.py")), "stdio"],
         }
     ) as server:
         # Initialise the agent with the server

agents_mcp_usage/basic_mcp/basic_mcp_use/pydantic_mcp.py

@@ -5,6 +5,8 @@
 from pydantic_ai import Agent
 from pydantic_ai.mcp import MCPServerStdio
 
+from agents_mcp_usage.utils import get_mcp_server_path
+
 load_dotenv()
 
 # Configure logging to logfire if LOGFIRE_TOKEN is set in environment
@@ -16,11 +18,11 @@
     command="uv",
     args=[
         "run",
-        "mcp_servers/example_server.py",
+        str(get_mcp_server_path("example_server.py")),
         "stdio",
     ],
 )
-agent = Agent("gemini-2.5-pro-preview-03-25", mcp_servers=[server])
+agent = Agent("gemini-2.5-pro-preview-06-05", mcp_servers=[server])
 Agent.instrument_all()