How to Use the Chameleon.io MCP in Google ADK

Q: Can I filter tools when using Chameleon.io with Google ADK?

Yes, use the toolnames filter in your configuration. This restricts which server functions your agent can actually execute.

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Connect Chameleon.io MCP to Google ADK

Create your Vinkius account to connect Chameleon.io 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 Chameleon.io with Google ADK

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Automate user segments using Google ADK

Run `list_user_segments` to see how your customers are grouped. Your agent uses this data to target specific product tours. Execute `get_experience_details` to pull technical specs for any active tour. This helps your agent explain the UI changes to your users.

Deep integration for Google ADK workflows

Your agent uses `list_experiences` to monitor what users see in your app. It identifies gaps in your current onboarding flow. Input results from `list_microsurvey_responses` into your BigQuery datasets. Your agent handles the transformation between raw survey data and your warehouse.

Event tracking for Google ADK agents

Send behavioral events with `track_user_event` during your agent's execution loop. It records user interactions in real-time. Verify these actions by calling `list_chameleon_events`. Your agent checks the logs to ensure data integrity across your stack.

Setup guide

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

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Common questions about Chameleon.io MCP in Google ADK

Yes, use the tool_names filter in your configuration. This restricts which server functions your agent can actually execute.

Absolutely. Gemini can ingest the data returned by these tools to reason about user behavior over long periods.

Provide your endpoint token to the managed server. The transport layer handles the secure handshake for your agent.

It supports both Stdio and HTTP transports. Choose the one that fits your Google Cloud infrastructure requirements.

Responses are fetched as JSON objects. Your agent processes these directly, ensuring no unauthorized access occurs to your raw user feedback.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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