How to Use the Zingtree MCP in AutoGen

Q: What specific data does Zingtree touch regarding privacy?

The server touches user session form data via getsessionformdata. Agents must be instructed to treat this information as sensitive during their deliberations.

Facilitate consensus-driven decisions with AutoGen agents and Zingtree data.

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Connect Zingtree MCP to AutoGen

Create your Vinkius account to connect Zingtree to AutoGen 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 AutoGen

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Claude

Perplexity

Agent Debate on Tree Structures using AutoGen

Set up a debate where one agent calls `get_tree_structure` to define the process. A second agent can then call `list_tree_variables` to challenge whether all necessary inputs are accounted for. This forces consensus on data completeness.

Multi-Agent Analysis of User Sessions with AutoGen

One agent retrieves session details using `get_session_details`, while a second agent uses `list_tree_sessions` to confirm the activity dates. The agents then negotiate whether the retrieved data fully covers the period in question.

Systematic Search across all Zingtree Workflows with AutoGen

Design a system where one agent searches for general workflows using `list_trees`. A second, specialized agent then executes `search_all_trees` to pinpoint the exact locations of keywords. The agents collaborate until they find a definitive answer.

Setup guide

Set up Zingtree MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Zingtree tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Zingtree_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zingtree data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Zingtree_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zingtree data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Zingtree MCP in AutoGen

You'll assign roles: one agent gathers the full tree structure using `get_tree_structure`, and a second agent verifies that all required variables are present via `list_tree_variables`. The debate converges on completeness.

Yes. You can define multi-step protocols: first, list trees to find the target area, then run session data retrieval (`get_session_form_data`) for validation.

It provides deep context on user paths. By listing all trees and their sessions, you can let agents debate the most common or overlooked decision flows in your system.

The tool set is designed for it. You can feed structured outputs from `get_session_details` and variable lists into agent discussions to force a conclusion about the process.

The server touches user session form data via `get_session_form_data`. Agents must be instructed to treat this information as sensitive during their deliberations.

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