How to Use the AfterLogic Aurora MCP in Google ADK

Q: Can Google ADK restrict which AfterLogic Aurora tools are active?

You bet. Apply the toolnames filter when configuring your toolset. This lets you expose read-only tools while blocking outbound actions.

Bring AfterLogic Aurora into your Google ADK pipelines. Feed entire mailboxes into Gemini's massive context window and act on BigQuery data.

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Connect AfterLogic Aurora MCP to Google ADK

Create your Vinkius account to connect AfterLogic Aurora 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 AfterLogic Aurora with Google ADK

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Claude

Perplexity

Process massive email histories

Gemini models process huge amounts of text effortlessly. You use `list_messages` to pull thousands of recent emails from an AfterLogic folder and dump them straight into a 1M+ token context window. The agent spots patterns across months of communication without breaking a sweat. It first runs `list_folders` to find the right directories, then processes the raw text to cross-reference against your existing Vertex AI datasets.

Map domains for Google ADK

Large deployments usually involve dozens of custom domains. Your Google agent calls `list_domains` to grab every active mapping on your instance. From there, you pipe that output directly into BigQuery. Add `check_account_exists` to the mix, and your agent audits provisioned email addresses against your employee database automatically.

Draft and send via MCP Server

Reading data only solves half the problem. The `send_email` tool allows your Gemini agent to compose and fire off messages through the Web API. You control exactly what the agent does by using the tool_names filter in your setup. If you want a read-only pipeline, simply exclude the send function when initializing your toolset.

Setup guide

Set up AfterLogic Aurora 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 AfterLogic Aurora 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="AfterLogic Aurora_agent",
    model="gemini-2.0-flash",
    instruction="You have access to AfterLogic Aurora tools via MCP.",
    tools=mcp_tools,
)

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Common questions about AfterLogic Aurora MCP in Google ADK

Install the google-adk package. Create an McpToolset using StreamableHttpServerParameters with your endpoint URL, then pass it to your LlmAgent.

You bet. Apply the tool_names filter when configuring your toolset. This lets you expose read-only tools while blocking outbound actions.

That is exactly what this setup does best. You pull massive email threads via the MCP connection and feed them straight into the model for deep reasoning.

Yes. Your agent monitors specific folders and writes the extracted data directly into your Google Cloud warehouse.

We rely on a strict zero-trust architecture. Whether your agent is pulling folder hierarchies or sending outbound messages, the connection happens through a temporary, isolated sandbox that drops all state immediately after execution.

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