How to Use the ChatBot.com MCP in Google ADK

Q: Can Gemini analyze ChatBot.com story interactions using Google ADK?

Yes. Your agent can call liststoryinteractions to load entire conversation paths directly into Gemini's long-context window for deep sentiment analysis.

Feed ChatBot.com conversation flows and user profiles directly into Gemini long-context runs using the Google ADK.

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Connect ChatBot.com MCP to Google ADK

Create your Vinkius account to connect ChatBot.com 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 ChatBot.com with Google ADK

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Analyze massive user history inside Gemini

Gemini-powered agents scan complete customer profiles extracted by the `list_chatbot_users` tool for behavioral patterns. By feeding this data into Gemini's massive context window, the agent spots trends that standard analytics platforms miss. You can combine this user data with your enterprise BigQuery datasets. This lets your Google ADK agent cross-reference offline purchase histories with live chat records to build highly accurate customer segments.

Map out ChatBot.com workflows using Google ADK

The `list_chatbot_stories` tool gives your agent a complete blueprint of your conversational workflows to analyze for logic bottlenecks. This MCP integration lets the agent read the structure, identify dead ends, and suggest improvements based on historical user paths. Vertex AI engines can process these story maps alongside your internal documentation. This ensures your automated chat flows always align with your latest corporate policies and product updates.

Fix unmatched NLP phrases with this MCP Server

Custom NLP matching rules are pulled directly into your agent's workspace for rapid auditing using the `list_chatbot_entities` tool. When users type phrases that the bot fails to match, the agent compares those inputs against existing entity lists to spot gaps. You can run this cleanup loop as a serverless Google Cloud Function. The agent pulls raw unrecognized phrases, matches them against your custom entities, and outputs clean training files ready for import.

Setup guide

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

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Common questions about ChatBot.com MCP in Google ADK

You initialize the toolset in Python using your server's HTTP endpoint. The ADK automatically exposes tools like `get_chatbot_user_details` to the Gemini model so it can query customer profiles on demand.

Yes. Your agent can call `list_story_interactions` to load entire conversation paths directly into Gemini's long-context window for deep sentiment analysis.

Yes, you can pass a specific list of tool names to your toolset configuration. This prevents the Gemini agent from touching sensitive tools like `list_chatbot_webhooks` if it only needs read access.

You certainly can. Your Python agent can fetch raw training phrases using `list_training_data` and write them directly to BigQuery for long-term storage and analytical reporting.

Your workflow stories and webhook configurations are processed entirely within a zero-trust sandbox. The server uses transient sessions, meaning your conversational schemas never touch persistent storage outside of your Google Cloud environment.

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