How to Use the BotGenuity MCP in Google ADK

Q: Can I use BotGenuity with Gemini long-context models in Google ADK?

Yes. The getconversationhistory tool can dump massive chat logs directly into Gemini's million-token context window for deep analysis.

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Connect BotGenuity MCP to Google ADK

Create your Vinkius account to connect BotGenuity 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 BotGenuity with Google ADK

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Feed BigQuery with data from this MCP Server

The `list_captured_leads` tool extracts user contact details and metadata directly from your active bots. Your Google ADK agent can immediately pipe these records into BigQuery for downstream marketing analysis. Because Gemini models support massive context windows, you can load thousands of leads at once. This setup avoids the rate limits that typically choke smaller models.

Analyze long chat histories in Google ADK

The `get_conversation_history` tool pulls complete transcript logs into your Gemini agent. This allows the model to analyze long-term customer sentiment across months of interactions. You do not have to write custom parsers for these logs. The MCP maps the tool outputs directly into your agent's context, letting you run complex reasoning tasks immediately.

Control your bot fleet from Vertex AI

The `list_chatbots` tool gives your enterprise agent a clean inventory of your support infrastructure. From there, the agent can use `get_chatbot_details` to verify that your system is running correctly. You can restrict which tools the agent can see using the ADK tool filter. This ensures your agent only reads data without executing unwanted actions.

Setup guide

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

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Common questions about BotGenuity MCP in Google ADK

Install the ADK and use the McpToolset class with your Vinkius HTTP URL. Pass that MCP toolset to your LlmAgent to expose all support tools to Gemini.

Yes. The `get_conversation_history` tool can dump massive chat logs directly into Gemini's million-token context window for deep analysis.

Absolutely. The connection runs through Vinkius's secure HTTPS endpoints, keeping your customer support data encrypted while in transit to Vertex AI.

Yes, the Google ADK lets you pass a specific list of tool names to the toolset. You can block tools like `chat_with_bot` if you only want the agent to read configurations.

Your customer contact records and chatbot configurations are processed in ephemeral V8 isolates. The MCP server never persists your support data, ensuring compliance with enterprise data policies.

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