How to Use the SmartChatAI MCP in Google ADK

Q: How does SmartChatAI and Google ADK handle bot provisioning?

You use createnewaibot to provision agents. The agent accepts a name and an optional starting prompt, giving you immediate control over the bot's initial identity.

Q: What sources of data can SmartChatAI and Google ADK train on?

The system supports PDF files (via addpdftoknowledgebase), raw text (addtexttoknowledgebase), or entire websites using the addwebsitetoknowledgebase tool. You can choose the best fit for your data.

Q: Can SmartChatAI and Google ADK list all active chatbots?

Yes, simply run listaichatbots. This command gives you a clear inventory of every bot managed under the MCP Server. It keeps your entire system organized.

Q: How can I check the health status when using SmartChatAI and Google ADK?

You run checkapihealth. This verifies that the API is up and running, which is critical before executing any mission-critical agent tasks.

Q: Which data type privacy considerations apply to SmartChatAI and Google ADK?

The server handles user profiles (getauthenticateduserprofile), full chat transcripts, and webhook configurations. Always review the PII handling for all ingested text or PDFs.

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Connect SmartChatAI MCP to Google ADK

Create your Vinkius account to connect SmartChatAI to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup SmartChatAI with Google ADK

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Create New AI Bots on the MCP Server

Need a dedicated agent for a specific workflow? Use `create_new_ai_bot` to provision it. You supply a name and an optional initial prompt, setting the stage for its core function. Once created, you can use `get_chatbot_details` to view its exact configuration and settings before deploying it into your Gemini pipeline.

Ingest Data from Websites

Instead of manually uploading documents, let the agent discover content using `scrape_domain_links`. This tool finds links across a domain that you specify. Once links are found, you can feed them into the knowledge base. Use `add_website_to_knowledge_base` to train your Gemini agents on live web data.

Manage and View Bot Activity

You'll need visibility into conversations. Call `get_bot_chat_history` to pull conversation transcripts, making it easy for your team to audit agent interactions. Want to know what bots exist? Run `list_ai_chatbots`. It gives you a clear list of every active bot running on the MCP Server.

Setup guide

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

You use `create_new_ai_bot` to provision agents. The agent accepts a name and an optional starting prompt, giving you immediate control over the bot's initial identity.

The system supports PDF files (via `add_pdf_to_knowledge_base`), raw text (`add_text_to_knowledge_base`), or entire websites using the `add_website_to_knowledge_base` tool. You can choose the best fit for your data.

Yes, simply run `list_ai_chatbots`. This command gives you a clear inventory of every bot managed under the MCP Server. It keeps your entire system organized.

You run `check_api_health`. This verifies that the API is up and running, which is critical before executing any mission-critical agent tasks.

The server handles user profiles (`get_authenticated_user_profile`), full chat transcripts, and webhook configurations. Always review the PII handling for all ingested text or PDFs.

Use it with your favorite AI tools

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