How to Use the Bot9 MCP in Google ADK

Q: Are Bot9 conversations accessible via Google ADK?

Yes, you can call listconversations or getconversationhistory to pull interaction data directly into your cloud-native workflow.

Q: Can I filter Bot9 tools in Google ADK?

You can. Use the toolnames filter to restrict which functions your agent can access, keeping your implementation scoped and secure.

Q: How do I check bot status in Google ADK using Bot9?

Simply trigger the getbot tool with the target ID. The server provides the current state of that specific agent back to your Python agent.

Use the Bot9 MCP Server with Google ADK to run support bots on your Google Cloud infrastructure.

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Connect Bot9 MCP to Google ADK

Create your Vinkius account to connect Bot9 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 Bot9 with Google ADK

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Syncing Google ADK with Bot9

Connect your Google Cloud agents to your support logic by passing the McpToolset to your LlmAgent. This allows your Gemini-backed agents to call `send_message` and retrieve replies instantly. Configuration is straightforward. Define your HTTP transport parameters and let the ADK manage the connection to the server.

Automating bot creation

Use `create_bot` to spin up new support instances as your traffic scales. Because you are using Google ADK, these bots can sit alongside your existing BigQuery data pipelines. Verify your current setup with `list_bots`. It returns a clean array of your active agents, making it easy to manage a large support presence across different products.

Knowledge base management

Point your bot at specific URLs using `add_data_source` to keep answers accurate. The Google ADK treats these as standard tools, allowing your agent to update its own knowledge base. Audit what your bots know by calling `list_data_sources`. It provides a snapshot of the URLs currently powering your support responses.

Setup guide

Set up Bot9 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 Bot9 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="Bot9_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Bot9 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 Bot9. 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 Bot9 MCP in Google ADK

You use the McpToolset class to wrap the server endpoint. This makes every Bot9 tool available to your Gemini-powered agent as a native function.

Yes, you can call `list_conversations` or `get_conversation_history` to pull interaction data directly into your cloud-native workflow.

You can. Use the tool_names filter to restrict which functions your agent can access, keeping your implementation scoped and secure.

Simply trigger the `get_bot` tool with the target ID. The server provides the current state of that specific agent back to your Python agent.

We treat all your conversation transcripts and bot training URLs as private. Your data stays within the encrypted transport layer during every API call.

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