Can my AI use this to query documents using vector embeddings directly?

Yes. If your agent is equipped with an embedding tool (like an OpenAI Ada dimension generator), it can compute the embedding float locally and transmit the precise K-Nearest Neighbors request into your Azure Index via the `vector_search` tool to return perfectly isolated contextual passages.

How can I verify if my Azure Search Indexer completed successfully?

You can ask your agent to list all indexers. It retrieves the scheduled background configurations defining how your Azure SQL or Blob stores migrate into Search form, allowing you to instantly assess if the pipeline is active or encountering extraction errors.

Can I audit the core configuration components of a specific index?

Absolutely. By providing the exact Index name, your AI fetches the exhaustive schema architecture: semantic mapping references, exact lexical BM25 fallback values, field weights, language analyzers, and HNSW graphs mapping vector space limits.

Azure AI Search MCP Server for Google ADK 6 tools — connect in under 2 minutes

Name: Azure AI Search
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 6 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Azure AI Search as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.

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

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token — get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="azure_ai_search_agent",
    instruction=(
        "You help users interact with Azure AI Search "
        "using 6 available tools."
    ),
    tools=[mcp_tools],
)

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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 Azure AI Search MCP Server

Connect your Azure AI Search endpoints to any AI agent and bring the power of enterprise RAG (Retrieval-Augmented Generation) directly into your conversational workflows.

Google ADK natively supports Azure AI Search as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 6 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Vector & Full-Text Search — Execute precise K-Nearest Neighbors (KNN) retrieval or perform deep lexical BM25 BM25 queries against millions of documents
Indexes & Schemas — List your search indexes and inspect structural schema definitions including analyzers, vector profiles, and semantic configurations
Data Sources — Extract REST maps detailing where your Azure indexers securely source unstructured data (CosmosDB, Blob Containers, Azure SQL)
Indexers — Audit and monitor your scheduled synchronization agents pulling continuous state transitions synchronously

The Azure AI Search MCP Server exposes 6 tools through the Vinkius. Connect it to Google ADK 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 Azure AI Search to Google ADK via MCP

Follow these steps to integrate the Azure AI Search MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 6 tools from Azure AI Search via MCP

Why Use Google ADK with the Azure AI Search MCP Server

Google ADK provides unique advantages when paired with Azure AI Search through the Model Context Protocol.

Google ADK natively supports MCP tool servers — declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Azure AI Search

Production-ready features like session management, evaluation, and deployment come built-in — not bolted on

Seamless integration with Google Cloud services means you can combine Azure AI Search tools with BigQuery, Vertex AI, and Cloud Functions

Azure AI Search + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Azure AI Search MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Azure AI Search and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Azure AI Search tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Azure AI Search regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Azure AI Search

Azure AI Search MCP Tools for Google ADK (6)

These 6 tools become available when you connect Azure AI Search to Google ADK via MCP:

get_index

Get explicit details of a single Azure search index configuration

list_datasources

List Azure AI Search data sources explicitly mapped

list_indexers

List explicit scheduled Azure indexer tasks

list_indexes

List all Azure AI Search indexes

search_documents

Execute lexical Full-Text search queries against Azure Indexes

vector_search

Highly targeted relevance extraction spanning dimensional maps. Perform Azure vector similarity searches via explicit embedding spaces

Example Prompts for Azure AI Search in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Azure AI Search immediately.

"Show me the configuration schema for our 'corporate-docs-v2' index."

"List the Azure Search indexers and tell me if any are failing."

"Run a full-text lexical search for 'Q3 Financial Audits' in the reports index."

Troubleshooting Azure AI Search MCP Server with Google ADK

Common issues when connecting Azure AI Search to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Azure AI Search + Google ADK FAQ

Common questions about integrating Azure AI Search MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.