How to Use the Azure AI Search MCP in Google ADK

Q: Can Google ADK filter which Azure AI Search MCP tools are exposed?

Yes. Use the toolnames parameter when setting up the toolset. You can restrict the agent to just vectorsearch if you want to prevent it from modifying or listing indexes.

Feed Azure AI Search results directly into Google ADK. Fill Gemini's massive context window with vector search data.

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Connect Azure AI Search MCP to Google ADK

Create your Vinkius account to connect Azure 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 Azure AI Search with Google ADK

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Perplexity

Massive RAG payloads via Google ADK

Gemini handles over a million tokens, which means you do not have to aggressively chunk your search results. Let your MCP agent run `search_documents` and dump massive text payloads straight into the prompt. The model digests the entire pile of context and reasons across it effortlessly. You get the raw retrieval power of Azure combined with Google's long-context architecture for deep analysis.

Cross-cloud data operations

Enterprise teams usually have data scattered everywhere. Your agent can pull semantic matches from Azure using `vector_search` and immediately compare them against records sitting in BigQuery. It audits the search infrastructure first via `list_indexes` to find the right endpoint. Connecting this MCP Server bridges the gap between Microsoft's search engine and Google's analytics backbone.

Inspect data sources and indexers

You need to know exactly where your embeddings originate. The agent executes `list_datasources` to map out exactly what blob storage or SQL database feeds the search engine. It then checks `list_indexers` to confirm the synchronization schedule. If Vertex AI needs fresh data for a pipeline, the agent verifies the Azure index is current before proceeding.

Setup guide

Set up Azure 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 Azure 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="Azure AI Search_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Azure AI Search tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Azure AI Search MCP in Google ADK

Initialize an MCP toolset using the Vinkius StreamableHttpServerParameters URL. Pass that object into the tools array of your LlmAgent setup.

Yes. Use the tool_names parameter when setting up the toolset. You can restrict the agent to just `vector_search` if you want to prevent it from modifying or listing indexes.

They work perfectly together. One agent can query your cloud indexes while another runs SQL in BigQuery, passing context back and forth through Gemini.

Vinkius handles the underlying Azure credentials. Your Google ADK script only needs a single endpoint token to execute searches securely.

Your index schemas and data source mappings remain strictly confidential. Every request routes through an ephemeral execution environment that immediately wipes memory after returning the JSON payload.

Use it with your favorite AI tools

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