How to Use the Zingtree MCP in LangChain

Q: How do I query session data using Zingtree and LangChain?

Your agent first calls listtreesessions to narrow down which sessions exist within a specific date range. From that list, you get the IDs needed to call getsessiondetails, giving your chain all the necessary context.

Q: Can LangChain help me map out Zingtree dependencies?

Yes. By chaining together calls like listtrees followed by gettreestructure, you can programmatically map the dependency and flow of your entire organization's decision trees. The output naturally feeds into the next step in your agent's logic.

Q: If I need to track historical usage, how does Zingtree work with LangChain?

You'll use listtreesessions by providing a date range and the tree identifier. This function returns a list of session UUIDs that your agent can then pass to other tools like getsessiondetails for deep historical analysis.

Build multi-step Zingtree workflows with LangChain, making every tool call a link in your reasoning chain.

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Create your Vinkius account to connect Zingtree to LangChain 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 LangChain

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Claude

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Automate workflow tracing.

Need to understand how users interact with decision trees? Start by calling `list_trees` to get an inventory of all available Zingtree workflows. Then, you can use `get_session_details` on a specific tree ID and session UUID. This lets your agent piece together the full context needed for deep analysis.

Analyze variable usage.

When debugging or auditing a process, knowing what variables were used matters. Run `list_tree_variables` to get an exhaustive list of every single variable within a tree. If you need the actual inputs, `get_session_form_data` grabs all form data submitted during any given session. This lets your agent build a complete picture of user input.

Search and structure retrieval.

Sometimes you just gotta know what's in the system. Use `search_all_trees` when you need to find specific text across every Zingtree workflow, regardless of its location. For deep dives, call `get_tree_structure`. This tool pulls the full, detailed structure for a specific tree, allowing your agent to process and act on the hierarchy.

Setup guide

Set up Zingtree MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Zingtree tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "zingtree-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Zingtree transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Zingtree MCP in LangChain

Your agent first calls `list_tree_sessions` to narrow down which sessions exist within a specific date range. From that list, you get the IDs needed to call `get_session_details`, giving your chain all the necessary context.

Yes. By chaining together calls like `list_trees` followed by `get_tree_structure`, you can programmatically map the dependency and flow of your entire organization's decision trees. The output naturally feeds into the next step in your agent's logic.

This MCP Server exposes structured workflow metadata, including session details, form inputs (`get_session_form_data`), and the complete variable list (`list_tree_variables`). Your agent can treat these distinct data sets as separate points of information.

You'll use `list_tree_sessions` by providing a date range and the tree identifier. This function returns a list of session UUIDs that your agent can then pass to other tools like `get_session_details` for deep historical analysis.

This server touches user session identifiers and form input data. When building your agent, remember that the outputs from tools like `get_session_form_data` contain potentially sensitive user-entered text.

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