How to Use the Zingtree MCP in Google ADK

Q: What is the best way to handle variable mapping with Zingtree using Google ADK?

You should use the listtreevariables tool. This function provides a definitive list of all variables available in your system, which you can then feed into BigQuery for further analysis within your agent pipeline.

Q: When building an enterprise workflow with Zingtree and Google ADK, what data is accessed?

The server accesses session details, form payloads (getsessionformdata), and the structural definition of trees. This allows your agent to understand both what was done and how the system works.

Q: Does Zingtree's MCP Server support retrieving the full structure for a given tree using Google ADK?

It does. The gettreestructure tool provides the complete, detailed map of any specific tree, giving your agent perfect knowledge of that workflow's logic.

Run enterprise analytics on workflows using the Google ADK framework.

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Connect Zingtree MCP to Google ADK

Create your Vinkius account to connect Zingtree 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 Zingtree with Google ADK

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Examine Full Workflow Structures via MCP Server

Use the `get_tree_structure` tool to map out a workflow's complete decision logic. It doesn't matter if your data lives in BigQuery or elsewhere; this server gives you the blueprint of the process. It also lets you run `list_trees`, giving your agent a high-level view of all available interactive trees across your entire organization.

Process Comprehensive User Session Data

The Google ADK can feed session data directly into Gemini models for massive context reasoning. Use `get_session_details` to pull comprehensive information on specific user activity. The server also exposes `list_tree_sessions`, letting you target data by date range. When combined with the 1M+ token context window, these sessions become powerful inputs for advanced analysis.

Identify All System Variables and Search Text

Your agent can identify every variable used across all processes using `list_tree_variables`. For broad research, the `search_all_trees` tool searches text content in every decision tree. This is key for finding specific knowledge buried deep within your workflow system. This entire set of Zingtree tools allows you to build enterprise agents that act directly on structured data sources.

Setup guide

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

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Common questions about Zingtree MCP in Google ADK

The Google ADK treats the MCP Server as a set of callable tools. Your agents use native integration points to execute functions like `get_session_form_data` and pass that structured data directly into Gemini models for reasoning.

Yes, the framework supports querying session data. You can use `list_tree_sessions` to filter activity by a specific date range and then get granular details with `get_session_details`.

You should use the `list_tree_variables` tool. This function provides a definitive list of all variables available in your system, which you can then feed into BigQuery for further analysis within your agent pipeline.

The server accesses session details, form payloads (`get_session_form_data`), and the structural definition of trees. This allows your agent to understand both *what* was done and *how* the system works.

It does. The `get_tree_structure` tool provides the complete, detailed map of any specific tree, giving your agent perfect knowledge of that workflow's logic.

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