How to Use the Vertex AI Search MCP in Google ADK

Q: How does Vertex AI Search work with the Google ADK?

The google-adk framework natively connects to the MCP tools on your Vertex AI Search endpoint. Because you're already in the Google ecosystem, passing data context is straightforward for complex reasoning.

Q: Can I list all my indexes using Vertex AI Search and the Google ADK?

Yes. Use listsearchengines to retrieve a complete inventory of your configured search engines. This confirms that your agent has access to all intended data sources.

Q: Does Vertex AI Search list document metadata with the Google ADK?

You can check indexed documents using listdatastoredocuments. You simply provide the necessary data store and branch IDs, and the tool returns the listing.

Enterprise Reasoning with Google ADK: Use Gemini to act on your private data via MCP Server tools.

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Connect Vertex AI Search MCP to Google ADK

Create your Vinkius account to connect Vertex AI Search to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Vertex AI Search with Google ADK

Ask AI about this MCP

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Claude

Perplexity

Get answers grounded in internal documents.

The `get_grounded_answer` tool is how you surface knowledge from proprietary sources. Your agent passes a query, and the server returns an answer derived directly from your private data's context. This means Gemini models are acting on your specific company information, not just general web knowledge.

Search across all enterprise documents.

Use `search_documents` when you need to execute a deep search query. The ADK passes the data store ID and the query text to this tool for processing. It runs Google's semantic search against your content. This mechanism ensures that complex, multi-part queries get accurate results from your entire enterprise knowledge base.

Find recommendations based on user actions.

To generate personalized suggestions, invoke `get_recommendations`. You must provide the data store ID and a JSON object detailing the user events. The system then uses this context to pull relevant records. This is critical for building truly adaptive agents that react to ongoing user behavior within Google Cloud infrastructure.

Setup guide

Set up Vertex AI Search MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Vertex AI Search tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Vertex AI Search_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Vertex AI Search tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Vertex AI Search MCP in Google ADK

The `google-adk` framework natively connects to the MCP tools on your Vertex AI Search endpoint. Because you're already in the Google ecosystem, passing data context is straightforward for complex reasoning.

This server touches configuration and metadata for private data stores. The core data types are document content, search query texts, and user event JSON objects passed to tools like `get_recommendations`.

Yes. Use `list_search_engines` to retrieve a complete inventory of your configured search engines. This confirms that your agent has access to all intended data sources.

You can check indexed documents using `list_datastore_documents`. You simply provide the necessary data store and branch IDs, and the tool returns the listing.

The server processes private data stored within your defined data stores. The model only receives access to document snippets or metadata required to answer a specific query, maintaining separation from the raw source.

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