Zingtree MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zingtree as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Zingtree. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Zingtree?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Zingtree MCP Server
Connect your Zingtree account to any AI agent to streamline your interactive workflows and decision tree management. This MCP server enables your agent to interact with trees, nodes, and detailed user session data directly from natural language.
LlamaIndex agents combine Zingtree tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Tree Oversight — List all interactive trees in your organization and retrieve their hierarchical structures
- Content Search — Search for specific text, keywords, or labels across all your nodes and workflows
- Session Analysis — Access detailed path data, browser info, and interaction history for any user session
- Form Data Extraction — Retrieve all values and answers entered by users during their tree interactions
- Historical Tracking — List sessions for specific trees within any date range to monitor performance and usage
The Zingtree MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Zingtree to LlamaIndex via MCP
Follow these steps to integrate the Zingtree MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Zingtree
Why Use LlamaIndex with the Zingtree MCP Server
LlamaIndex provides unique advantages when paired with Zingtree through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zingtree tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zingtree tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zingtree, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zingtree tools were called, what data was returned, and how it influenced the final answer
Zingtree + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zingtree MCP Server delivers measurable value.
Hybrid search: combine Zingtree real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zingtree to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Zingtree for fresh data
Analytical workflows: chain Zingtree queries with LlamaIndex's data connectors to build multi-source analytical reports
Zingtree MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Zingtree to LlamaIndex via MCP:
get_clean_session_path
Get a clean linear path for a user session
get_session_details
Get detailed data for a specific user session
get_session_form_data
Get all form data entered during a session
get_tree_structure
Get the full structure of a specific tree
list_tree_sessions
List sessions for a tree within a date range
list_tree_variables
List all variables used in a tree
list_trees
List all interactive trees in the organization
search_all_trees
Search for text within all decision trees
Example Prompts for Zingtree in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zingtree immediately.
"List all decision trees in my Zingtree account."
"Show me the structure for tree ID '12345'."
"Get the form data for session ID 'XYZ-987-ABC'."
Troubleshooting Zingtree MCP Server with LlamaIndex
Common issues when connecting Zingtree to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZingtree + LlamaIndex FAQ
Common questions about integrating Zingtree MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Zingtree with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Zingtree to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.