Can I get direct answers from my documents without reading through them?

Yes. Using the `get_grounded_answer` tool, your AI agent can process a natural language question and return a precise answer based specifically on the content within your Vertex AI Search data stores. This grounding ensures high accuracy and reduces hallucinations by sticking to your private data as the source of truth.

How do I know which data stores are available to search?

Ask your agent to list your data stores. It will return all configured data stores in your collection along with their IDs and names. You can then use these IDs to perform targeted semantic searches or browse specific document branches.

Can I use this for product recommendations on my website?

Absolutely. The `get_recommendations` tool allows your agent to retrieve personalized recommendations by providing user event data. This is ideal for testing recommendation engines and surfacing relevant content or products to users based on their historical behavior.

Vertex AI Search MCP Server for AutoGen 7 tools — connect in under 2 minutes

Name: Vertex AI Search
Availability: InStock
Author: Vinkius

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 7 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Vertex AI Search as an MCP tool provider through the Vinkius and every agent in the group can access live data and take action.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="vertex_ai_search_agent",
            tools=tools,
            system_message=(
                "You help users with Vertex AI Search. "
                "7 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Vertex AI Search MCP Server

Connect your Vertex AI Search account to any AI agent and harness the power of Google's semantic search technology on your own enterprise data through natural conversation.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Vertex AI Search tools. Connect 7 tools through the Vinkius and assign role-based access — a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

What you can do

Semantic Search — Perform high-quality semantic searches across documents with AI-powered relevance and accuracy
Grounded Answers — Get direct, natural language answers grounded in your private document collection for reliable Q&A
Data Stores — List and browse your enterprise data stores and search engines to manage your searchable datasets
Document Discovery — Browse and list indexed documents within your data store branches directly from your agent
Personalized Recommendations — Retrieve intelligent recommendations based on user interaction events and patterns
Search Engines — View and manage high-level search applications configured for specific business use cases

The Vertex AI Search MCP Server exposes 7 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Vertex AI Search to AutoGen via MCP

Follow these steps to integrate the Vertex AI Search MCP Server with AutoGen.

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 7 tools from Vertex AI Search automatically

Why Use AutoGen with the Vertex AI Search MCP Server

AutoGen provides unique advantages when paired with Vertex AI Search through the Model Context Protocol.

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Vertex AI Search tools to solve complex tasks

Role-based architecture lets you assign Vertex AI Search tool access to specific agents — a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive Vertex AI Search tool calls

Code execution sandbox: AutoGen agents can write and run code that processes Vertex AI Search tool responses in an isolated environment

Vertex AI Search + AutoGen Use Cases

Practical scenarios where AutoGen combined with the Vertex AI Search MCP Server delivers measurable value.

Collaborative analysis: one agent queries Vertex AI Search while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from Vertex AI Search, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using Vertex AI Search data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process Vertex AI Search responses in a sandboxed execution environment

Vertex AI Search MCP Tools for AutoGen (7)

These 7 tools become available when you connect Vertex AI Search to AutoGen via MCP:

get_datastore_details

Retrieves configuration and metadata for a specific data store

get_grounded_answer

Returns a natural language response based on your private data. Retrieves an AI-generated answer grounded in the documents of a data store

get_recommendations

Provide a data store ID and user event data as a JSON object. Retrieves personalized recommendations based on user events

list_data_stores

Lists all data stores in the Vertex AI Search collection

list_datastore_documents

Provide data store and branch IDs. Lists all indexed documents within a specific data store branch

list_search_engines

Lists all search engines configured in the collection

search_documents

Provide a data store ID and the query text. Performs a search query across documents in a specific data store

Example Prompts for Vertex AI Search in AutoGen

Ready-to-use prompts you can give your AutoGen agent to start working with Vertex AI Search immediately.

"List all my available data stores in Vertex AI Search."

"Based on our documentation, what is our remote work policy?"

"Search the product catalog for 'blue wireless headphones'."

Troubleshooting Vertex AI Search MCP Server with AutoGen

Common issues when connecting Vertex AI Search to AutoGen through the Vinkius, and how to resolve them.

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

Vertex AI Search + AutoGen FAQ

Common questions about integrating Vertex AI Search MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Vertex AI Search tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.