How to Use the Vertex AI Search MCP in AutoGen

Q: Can AutoGen use Vertex AI Search to check document sources?

Yes. The agents can call listdatastoredocuments using specified data store and branch IDs, confirming that specific documents exist before debating their content or validity.

Q: What kind of data does Vertex AI Search handle with AutoGen?

It handles both indexed documents (via searchdocuments) and user event data (for personalized recommendations). The consensus decision often draws from a mix of these sources.

Q: Does the MCP Server provide enough context for multi-agent use with AutoGen?

Absolutely. Calling listdatastores gives all agents a single source of truth about which data collections are available in the entire Vertex AI Search environment.

Q: What does Vertex AI Search touch regarding data privacy when used with AutoGen?

It touches documents and user event data. The agents must manage these inputs carefully to ensure that only authorized, private information is included in the debate.

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Setup Vertex AI Search with AutoGen

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Perplexity

Facilitating debate through the MCP Server

When your agents need a decision, they can use `get_grounded_answer` to gather facts. Multiple specialized agents can then debate these facts until they reach consensus on a single, supported answer. This process is ideal for complex problems where the solution requires deliberation between competing viewpoints.

Validating data sources with Vertex AI Search and AutoGen

If an agent needs to verify its starting assumptions, it first calls `list_data_stores` to get a full inventory of available knowledge. Agents can then use `get_datastore_details` to confirm the configuration of any source they are debating over. This ensures that every piece of evidence used during debate is properly sourced and configured.

Contextualizing search results

Beyond simple keyword searches, agents can use `get_recommendations` after retrieving basic data. This gives the agent a layer of personalization—it's not just what was searched, but what is relevant to the user’s behavior. This allows your multi-agent system to move from general information retrieval to highly targeted advice.

Setup guide

Set up Vertex AI Search MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Vertex AI Search tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Vertex AI Search_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Vertex AI Search data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Vertex AI Search_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Vertex AI Search data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vertex AI Search. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Vertex AI Search MCP in AutoGen

The MCP Server provides the evidence base. Agents use tools like `search_documents` and `get_grounded_answer` to gather facts, which then become the points of contention or agreement during agent discussion.

Yes. The agents can call `list_datastore_documents` using specified data store and branch IDs, confirming that specific documents exist before debating their content or validity.

It handles both indexed documents (via `search_documents`) and user event data (for personalized recommendations). The consensus decision often draws from a mix of these sources.

Absolutely. Calling `list_data_stores` gives all agents a single source of truth about which data collections are available in the entire Vertex AI Search environment.

It touches **documents** and **user event data**. The agents must manage these inputs carefully to ensure that only authorized, private information is included in the debate.

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