chore: Update toolbox-adk dependency to pick the latest version

## Description

Samples for MCP with Neo4j for this page:
https://googleapis.github.io/genai-toolbox/samples/

## PR Checklist

> Thank you for opening a Pull Request! Before submitting your PR, there
are a
> few things you can do to make sure it goes smoothly:

- [x] Make sure you reviewed

[CONTRIBUTING.md](https://github.com/googleapis/genai-toolbox/blob/main/CONTRIBUTING.md)
- [ ] Make sure to open an issue as a

[bug/issue](https://github.com/googleapis/genai-toolbox/issues/new/choose)
  before writing your code! That way we can discuss the change, evaluate
  designs, and agree on the general idea
- [x] Ensure the tests and linter pass
- [x] Code coverage does not decrease (if any source code was changed)
- [x] Appropriate docs were updated (if necessary)
- [x] Make sure to add `!` if this involve a breaking change

---------

Co-authored-by: Yuan Teoh <45984206+Yuan325@users.noreply.github.com>

docs/en/getting-started/colab_quickstart.ipynb

@@ -509,7 +509,7 @@
       },
       "outputs": [],
       "source": [
-        "! pip install toolbox-core --quiet\n",
+        "! pip install toolbox-adk --quiet\n",
         "! pip install google-adk --quiet"
       ]
     },
@@ -525,14 +525,18 @@
         "from google.adk.runners import Runner\n",
         "from google.adk.sessions import InMemorySessionService\n",
         "from google.adk.artifacts.in_memory_artifact_service import InMemoryArtifactService\n",
+        "from google.adk.tools.toolbox_toolset import ToolboxToolset\n",
         "from google.genai import types\n",
-        "from toolbox_core import ToolboxSyncClient\n",
         "\n",
         "import os\n",
         "# TODO(developer): replace this with your Google API key\n",
         "os.environ['GOOGLE_API_KEY'] = \"<GOOGLE_API_KEY>\"\n",
         "\n",
-        "toolbox_client = ToolboxSyncClient(\"http://127.0.0.1:5000\")\n",
+        "# Configure toolset\n",
+        "toolset = ToolboxToolset(\n",
+        "    server_url=\"http://127.0.0.1:5000\",\n",
+        "    toolset_name=\"my-toolset\"\n",
+        ")\n",
         "\n",
         "prompt = \"\"\"\n",
         "  You're a helpful hotel assistant. You handle hotel searching, booking and\n",
@@ -549,7 +553,7 @@
         "    name='hotel_agent',\n",
         "    description='A helpful AI assistant.',\n",
         "    instruction=prompt,\n",
-        "    tools=toolbox_client.load_toolset(\"my-toolset\"),\n",
+        "    tools=[toolset],\n",
         ")\n",
         "\n",
         "session_service = InMemorySessionService()\n",

docs/en/getting-started/local_quickstart.md

@@ -52,7 +52,7 @@ runtime](https://research.google.com/colaboratory/local-runtimes.html).
     {{< tabpane persist=header >}}
 {{< tab header="ADK" lang="bash" >}}
 
-pip install toolbox-core
+pip install toolbox-adk
 {{< /tab >}}
 {{< tab header="Langchain" lang="bash" >}}
 

docs/en/getting-started/quickstart/python/adk/quickstart.py

@@ -1,15 +1,17 @@
 from google.adk import Agent
 from google.adk.apps import App
-from toolbox_core import ToolboxSyncClient
+from google.adk.tools.toolbox_toolset import ToolboxToolset
 
 # TODO(developer): update the TOOLBOX_URL to your toolbox endpoint
-client = ToolboxSyncClient("http://127.0.0.1:5000")
+toolset = ToolboxToolset(
+    server_url="http://127.0.0.1:5000",
+)
 
 root_agent = Agent(
     name='root_agent',
     model='gemini-2.5-flash',
     instruction="You are a helpful AI assistant designed to provide accurate and useful information.",
-    tools=client.load_toolset(),
+    tools=[toolset],
 )
 
 app = App(root_agent=root_agent, name="my_agent")

docs/en/getting-started/quickstart/python/adk/requirements.txt

@@ -1,3 +1,3 @@
 google-adk==1.21.0
-toolbox-core==0.5.4
+toolbox-adk>=0.1.0
 pytest==9.0.2

docs/en/how-to/deploy_adk_agent.md

@@ -49,7 +49,7 @@ with the necessary configuration for deployment to Vertex AI Agent Engine.
 4.  Add `toolbox-core` as a dependency to the new project:
 
     ```bash
-    uv add toolbox-core
+    uv add toolbox-adk
     ```
 
 ## Step 3: Configure Google Cloud Authentication
@@ -95,22 +95,23 @@ authentication token.
     ```python
     from google.adk import Agent
     from google.adk.apps import App
-    from toolbox_core import ToolboxSyncClient, auth_methods
+    from google.adk.tools.toolbox_toolset import ToolboxToolset
+    from toolbox_adk import CredentialStrategy
 
     # TODO(developer): Replace with your Toolbox Cloud Run Service URL
     TOOLBOX_URL = "https://your-toolbox-service-xyz.a.run.app"
 
-    # Initialize the client with the Cloud Run URL and Auth headers
-    client = ToolboxSyncClient(
-        TOOLBOX_URL, 
-        client_headers={"Authorization": auth_methods.get_google_id_token(TOOLBOX_URL)}
+    # Initialize the toolset with Workload Identity (generates ID token for the URL)
+    toolset = ToolboxToolset(
+        server_url=TOOLBOX_URL,
+        credentials=CredentialStrategy.workload_identity(target_audience=TOOLBOX_URL)
     )
 
     root_agent = Agent(
         name='root_agent',
         model='gemini-2.5-flash',
         instruction="You are a helpful AI assistant designed to provide accurate and useful information.",
-        tools=client.load_toolset(),
+        tools=[toolset],
     )
 
     app = App(root_agent=root_agent, name="my_agent")

docs/en/resources/tools/redis/redis.md

@@ -30,10 +30,6 @@ following config for example:
     - name: userNames
       type: array
       description: The user names to be set.  
-      items:
-          name: userName # the item name doesn't matter but it has to exist
-          type: string 
-          description: username
 ```
 
 If the input is an array of strings `["Alice", "Sid", "Bob"]`,  The final command

docs/en/samples/bigquery/local_quickstart.md

@@ -365,7 +365,7 @@ pip install llama-index-llms-google-genai
 
 {{< /tab >}}
 {{< tab header="ADK" lang="bash" >}}
-pip install toolbox-core
+pip install toolbox-adk
 {{< /tab >}}
 {{< /tabpane >}}
 
@@ -607,8 +607,8 @@ from google.adk.agents import Agent
 from google.adk.runners import Runner
 from google.adk.sessions import InMemorySessionService
 from google.adk.artifacts.in_memory_artifact_service import InMemoryArtifactService
+from google.adk.tools.toolbox_toolset import ToolboxToolset
 from google.genai import types # For constructing message content
-from toolbox_core import ToolboxSyncClient
 
 import os
 os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = 'True'
@@ -623,48 +623,47 @@ os.environ['GOOGLE_CLOUD_LOCATION'] = 'us-central1'
 
 # --- Load Tools from Toolbox ---
 
-# TODO(developer): Ensure the Toolbox server is running at <http://127.0.0.1:5000>
+# TODO(developer): Ensure the Toolbox server is running at http://127.0.0.1:5000
+toolset = ToolboxToolset(server_url="http://127.0.0.1:5000")
 
-with ToolboxSyncClient("<http://127.0.0.1:5000>") as toolbox_client:
-    # TODO(developer): Replace "my-toolset" with the actual ID of your toolset as configured in your MCP Toolbox server.
-    agent_toolset = toolbox_client.load_toolset("my-toolset")
+# --- Define the Agent's Prompt ---
+prompt = """
+  You're a helpful hotel assistant. You handle hotel searching, booking and
+  cancellations. When the user searches for a hotel, mention it's name, id,
+  location and price tier. Always mention hotel ids while performing any
+  searches. This is very important for any operations. For any bookings or
+  cancellations, please provide the appropriate confirmation. Be sure to
+  update checkin or checkout dates if mentioned by the user.
+  Don't ask for confirmations from the user.
+"""
 
-    # --- Define the Agent's Prompt ---
-    prompt = """
-      You're a helpful hotel assistant. You handle hotel searching, booking and
-      cancellations. When the user searches for a hotel, mention it's name, id,
-      location and price tier. Always mention hotel ids while performing any
-      searches. This is very important for any operations. For any bookings or
-      cancellations, please provide the appropriate confirmation. Be sure to
-      update checkin or checkout dates if mentioned by the user.
-      Don't ask for confirmations from the user.
-    """
+# --- Configure the Agent ---
 
-    # --- Configure the Agent ---
+root_agent = Agent(
+    model='gemini-2.0-flash-001',
+    name='hotel_agent',
+    description='A helpful AI assistant that can search and book hotels.',
+    instruction=prompt,
+    tools=[toolset], # Pass the loaded toolset
+)
 
-    root_agent = Agent(
-        model='gemini-2.0-flash-001',
-        name='hotel_agent',
-        description='A helpful AI assistant that can search and book hotels.',
-        instruction=prompt,
-        tools=agent_toolset, # Pass the loaded toolset
-    )
+# --- Initialize Services for Running the Agent ---
+session_service = InMemorySessionService()
+artifacts_service = InMemoryArtifactService()
 
-    # --- Initialize Services for Running the Agent ---
-    session_service = InMemorySessionService()
-    artifacts_service = InMemoryArtifactService()
+runner = Runner(
+    app_name='hotel_agent',
+    agent=root_agent,
+    artifact_service=artifacts_service,
+    session_service=session_service,
+)
+
+async def main():
     # Create a new session for the interaction.
-    session = session_service.create_session(
+    session = await session_service.create_session(
         state={}, app_name='hotel_agent', user_id='123'
     )
 
-    runner = Runner(
-        app_name='hotel_agent',
-        agent=root_agent,
-        artifact_service=artifacts_service,
-        session_service=session_service,
-    )
-
     # --- Define Queries and Run the Agent ---
     queries = [
         "Find hotels in Basel with Basel in it's name.",
@@ -687,6 +686,10 @@ with ToolboxSyncClient("<http://127.0.0.1:5000>") as toolbox_client:
 
         for text in responses:
           print(text)
+
+import asyncio
+if __name__ == "__main__":
+    asyncio.run(main())
 {{< /tab >}}
 {{< /tabpane >}}
 

docs/en/samples/neo4j/_index.md

@@ -0,0 +1,7 @@
+---
+title: "Neo4j"
+type: docs
+weight: 1
+description: >
+  How to get started with Toolbox using Neo4j.
+---

docs/en/samples/neo4j/mcp_quickstart.md

@@ -0,0 +1,141 @@
+---
+title: "Quickstart (MCP with Neo4j)"
+type: docs
+weight: 1
+description: >
+  How to get started running Toolbox with MCP Inspector and Neo4j as the source.
+---
+
+## Overview
+
+[Model Context Protocol](https://modelcontextprotocol.io) is an open protocol that standardizes how applications provide context to LLMs. Check out this page on how to [connect to Toolbox via MCP](../../how-to/connect_via_mcp.md).
+
+
+## Step 1: Set up your Neo4j Database and Data
+
+In this section, you'll set up a database and populate it with sample data for a movies-related agent. This guide assumes you have a running Neo4j instance, either locally or in the cloud.
+
+. **Populate the database with data.**
+To make this quickstart straightforward, we'll use the built-in Movies dataset available in Neo4j.
+
+. In your Neo4j Browser, run the following command to create and populate the database:
++
+```cypher
+:play movies
+````
+
+. Follow the instructions to load the data. This will create a graph with `Movie`, `Person`, and `Actor` nodes and their relationships.
+
+
+## Step 2: Install and configure Toolbox
+
+In this section, we will install the MCP Toolbox, configure our tools in a `tools.yaml` file, and then run the Toolbox server.
+
+. **Install the Toolbox binary.**
+The simplest way to get started is to download the latest binary for your operating system.
+
+. Download the latest version of Toolbox as a binary:
+\+
+
+```bash
+export OS="linux/amd64" # one of linux/amd64, darwin/arm64, darwin/amd64, or windows/amd64
+curl -O [https://storage.googleapis.com/genai-toolbox/v0.16.0/$OS/toolbox](https://storage.googleapis.com/genai-toolbox/v0.16.0/$OS/toolbox)
+```
+
+  + 
+. Make the binary executable:
+\+
+
+```bash
+chmod +x toolbox
+```
+
+. **Create the `tools.yaml` file.**
+This file defines your Neo4j source and the specific tools that will be exposed to your AI agent.
+\+
+{{\< notice tip \>}}
+Authentication for the Neo4j source uses standard username and password fields. For production use, it is highly recommended to use environment variables for sensitive information like passwords.
+{{\< /notice \>}}
+\+
+Write the following into a `tools.yaml` file:
+\+
+
+```yaml
+sources:
+  my-neo4j-source:
+    kind: neo4j
+    uri: bolt://localhost:7687
+    user: neo4j
+    password: my-password # Replace with your actual password
+
+tools:
+  search-movies-by-actor:
+    kind: neo4j-cypher
+    source: my-neo4j-source
+    description: "Searches for movies an actor has appeared in based on their name. Useful for questions like 'What movies has Tom Hanks been in?'"
+    parameters:
+      - name: actor_name
+        type: string
+        description: The full name of the actor to search for.
+    statement: |
+      MATCH (p:Person {name: $actor_name}) -[:ACTED_IN]-> (m:Movie)
+      RETURN m.title AS title, m.year AS year, m.genre AS genre
+  
+  get-actor-for-movie:
+    kind: neo4j-cypher
+    source: my-neo4j-source
+    description: "Finds the actors who starred in a specific movie. Useful for questions like 'Who acted in Inception?'"
+    parameters:
+      - name: movie_title
+        type: string
+        description: The exact title of the movie.
+    statement: |
+      MATCH (p:Person) -[:ACTED_IN]-> (m:Movie {title: $movie_title})
+      RETURN p.name AS actor
+```
+
+. **Start the Toolbox server.**
+Run the Toolbox server, pointing to the `tools.yaml` file you created earlier.
+\+
+
+```bash
+./toolbox --tools-file "tools.yaml"
+```
+
+## Step 3: Connect to MCP Inspector
+
+. **Run the MCP Inspector:**
+\+
+
+```bash
+npx @modelcontextprotocol/inspector
+```
+
+. Type `y` when it asks to install the inspector package.
+. It should show the following when the MCP Inspector is up and running (please take note of `<YOUR_SESSION_TOKEN>`):
+\+
+
+```bash
+Starting MCP inspector...
+⚙️ Proxy server listening on localhost:6277
+🔑 Session token: <YOUR_SESSION_TOKEN>
+    Use this token to authenticate requests or set DANGEROUSLY_OMIT_AUTH=true to disable auth
+
+🚀 MCP Inspector is up and running at:
+    http://localhost:6274/?MCP_PROXY_AUTH_TOKEN=<YOUR_SESSION_TOKEN>
+```
+
+1. Open the above link in your browser.
+
+1. For `Transport Type`, select `Streamable HTTP`.
+
+1. For `URL`, type in `http://127.0.0.1:5000/mcp`.
+
+1. For `Configuration` -\> `Proxy Session Token`, make sure `<YOUR_SESSION_TOKEN>` is present.
+
+1. Click `Connect`.
+
+1. Select `List Tools`, you will see a list of tools configured in `tools.yaml`.
+
+1. Test out your tools here\!
+