How to Use the ChatGen MCP in Google ADK

Q: How do I initialize the ChatGen MCP Server in Google ADK?

You instantiate McpToolset using the StreamableHttpServerParameters pointing to your Vinkius endpoint. Pass this toolset directly into the LlmAgent tools array. Gemini will auto-discover the tools immediately.

Q: How does ChatGen handle high-volume lead analysis with Google ADK?

The agent calls listconversations to ingest chat logs, utilizing Gemini's large context window to process them. It then extracts key qualifiers and saves them using your cloud database integrations.

Feed ChatGen lead data and bot metrics directly to your Gemini agents using the Google ADK.

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Connect ChatGen MCP to Google ADK

Create your Vinkius account to connect ChatGen 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 ChatGen with Google ADK

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Run ChatGen operations with Google ADK

The `list_conversations` tool feeds raw chat logs directly into your Google ADK agent for long-context analysis. Gemini's massive context window parses thousands of these interactions to extract customer sentiment trends. You can write these processed insights directly into BigQuery. The agent uses `get_bot` to match conversation patterns with specific bot configurations, updating your marketing models instantly.

Map support routing via Google ADK MCP Server

The `list_teams` tool queries your organizational structure so your Google ADK agent can route leads accurately. When a prospect gets flagged, the agent maps their needs to active teams retrieved via this tool. This keeps your routing logic dynamic and grounded in real cloud database records via the MCP server. The agent matches team availability with lead profiles without hardcoded scripts.

Automate bot lifecycle management

The `create_bot` tool allows your Google ADK agent to spin up fresh conversational instances based on cloud triggers. If Vertex AI detects a traffic spike on a new landing page, the agent runs this tool to deploy a targeted bot. It can later clean up stale instances using `delete_bot` or tweak parameters with `update_bot`. This keeps your user-facing chat footprint lean and highly relevant to active campaigns.

Setup guide

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

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Common questions about ChatGen MCP in Google ADK

You instantiate `McpToolset` using the `StreamableHttpServerParameters` pointing to your Vinkius endpoint. Pass this toolset directly into the `LlmAgent` tools array. Gemini will auto-discover the tools immediately.

Yes, you can filter tools during initialization. Use the optional `tool_names` parameter to only expose tools like `list_leads` or `get_lead_details` while blocking destructive tools like `delete_bot`.

The agent calls `list_conversations` to ingest chat logs, utilizing Gemini's large context window to process them. It then extracts key qualifiers and saves them using your cloud database integrations.

No, the toolkit automatically translates MCP schemas into Google-compatible tool definitions. Your agent understands the parameters for tools like `update_bot` without extra code.

All lead details, bot configurations, and team layouts are processed within ephemeral V8 sandboxes. Your Google Cloud environment only receives data requested by specific tool calls like `get_lead_details`, ensuring no raw database exposure.

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