How to Use the Azure Service Bus Topic MCP in Google ADK

Q: Is this setup compatible with long-context reasoning in Google ADK?

It is. You can process large datasets and use publishmessage to send summary events to your infrastructure once the agent completes its analysis.

Connect Google ADK agents to Azure Service Bus Topics for enterprise event orchestration.

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Connect Azure Service Bus Topic MCP to Google ADK

Create your Vinkius account to connect Azure Service Bus Topic 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 Service Bus Topic with Google ADK

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Native event integration for Google ADK

The `publish_message` tool lets your agent talk to your bus infrastructure. You register the toolset once, and the agent handles the rest. It fits right into your existing Google Cloud environment. You no longer need separate scripts to bridge your agent's reasoning to external cloud services.

High-fidelity message routing

You dictate message flow by passing customProperties to `publish_message`. Your agent dynamically decides the routing tags based on the task at hand. This allows for granular control over how your subscribers receive data. You map agent output to specific business logic paths without extra configuration.

Unified tool management

Google ADK users can filter tool access to keep the agent focused. You expose only the messaging capability needed for your specific deployment. This keeps your agent's tool list clean. It simplifies debugging when you only have one function responsible for cloud delivery.

Setup guide

Set up Azure Service Bus Topic 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 Service Bus Topic 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 Service Bus Topic_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Azure Service Bus Topic tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Azure Service Bus Topic MCP in Google ADK

You initialize the McpToolset with your server parameters and pass it into your agent's tool list. This makes the messaging function available to the Gemini model.

It does. You can choose between Stdio or HTTP depending on whether your agent runs locally or as a cloud-based service.

You manage authentication through the Vinkius endpoint token. The server handles the connection to the bus so your agent code remains clean.

It is. You can process large datasets and use `publish_message` to send summary events to your infrastructure once the agent completes its analysis.

All communication occurs over encrypted channels. The server acts as a transient bridge for your message payloads, ensuring no persistent data is held on our side.

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