How to Use the GPTBots MCP in Google ADK

Q: Can I filter GPTBots tools for Google ADK?

You can use the toolnames filter in your McpToolset configuration. This restricts which server functions your agent can see, limiting the scope of its actions.

Integrate GPTBots with your Google ADK agents to sync workflows and databases within your enterprise cloud architecture.

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Connect GPTBots MCP to Google ADK

Create your Vinkius account to connect GPTBots 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 GPTBots with Google ADK

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Execute workflows via Google ADK

Use `trigger_workflow` to kick off automated tasks from your Gemini-powered agents. Once initiated, you call `query_workflow` to track completion status and handle the output within your Google Cloud environment. This setup works well for enterprise automation. It links your agent's reasoning capabilities directly to the operational tasks managed by your bots.

Query internal platform databases

Map out your data architecture by using `list_databases`. This tool gives your Google ADK agent the visibility needed to identify which tables are available for interaction. It removes the guesswork from agent data access. Your agent knows exactly where to look, keeping your infrastructure logic deterministic and easy to maintain.

Send messages and manage chats

Directly interact with your users by calling `send_bot_message` from your agent logic. You can review current threads with `list_conversations` to ensure your Google ADK agent has the right context. This creates a tighter feedback loop for your support or operations agents. It ensures that every response sent via your infrastructure is logged and retrievable.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by GPTBots. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about GPTBots MCP in Google ADK

Data privacy is handled at the transport layer. The MCP server uses encrypted connections to ensure that your database queries remain private between your agent and the GPTBots platform.

You can use the tool_names filter in your McpToolset configuration. This restricts which server functions your agent can see, limiting the scope of its actions.

The server response time is optimized for high-throughput environments. You should monitor your agent's execution traces to ensure that tool calls align with your performance requirements.

The server supports both Stdio and HTTP transports. You choose the one that fits your deployment architecture in Google Cloud.

The server interacts with conversation history, workflow status metadata, and knowledge base documents. It does not access your underlying BigQuery data unless you explicitly pipe those results into a knowledge document.

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