Zengain MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Get Analytics Summary, Get Health Score, Get Product, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zengain 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 App Connector for LlamaIndex
The Zengain app connector for LlamaIndex is a standout in the Data Analytics category — giving your AI agent 10 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Zengain. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Zengain?"
)
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 Zengain MCP Server
Connect your Zengain (Nalpeiron Growth Platform) account to any AI agent and simplify your customer success and usage analytics workflows through natural conversation.
LlamaIndex agents combine Zengain tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Product Lifecycle — List all registered products and retrieve detailed configuration metadata
- User Engagement — Query product users, inspect their profiles, and calculate real-time health scores
- Usage Analytics — Get high-level analytics summaries and track custom events to monitor feature adoption
- KPM Tracking — Monitor Key Product Milestones to identify successful onboarding and churn risks
- System Monitoring — List configured webhooks to understand your integration data flow
The Zengain MCP Server exposes 10 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.
All 10 Zengain tools available for LlamaIndex
When LlamaIndex connects to Zengain through Vinkius, your AI agent gets direct access to every tool listed below — spanning customer-success, product-analytics, lead-scoring, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Get analytics summary
Get customer health score
Get details for a specific product
Get details for a specific user
List tracking events
List Key Product Milestones
List Zengain products
List product users
List configured webhooks
Track a custom event
Connect Zengain to LlamaIndex via MCP
Follow these steps to wire Zengain into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Zengain MCP Server
LlamaIndex provides unique advantages when paired with Zengain through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zengain tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zengain tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zengain, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zengain tools were called, what data was returned, and how it influenced the final answer
Zengain + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zengain MCP Server delivers measurable value.
Hybrid search: combine Zengain real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zengain 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 Zengain for fresh data
Analytical workflows: chain Zengain queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Zengain in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zengain immediately.
"List all products in my Zengain account."
"What is the health score for user 'customer_456'?"
"Show me a summary of usage analytics for this month."
Troubleshooting Zengain MCP Server with LlamaIndex
Common issues when connecting Zengain to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZengain + LlamaIndex FAQ
Common questions about integrating Zengain MCP Server with LlamaIndex.