Zenedu MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zenedu as an MCP tool provider through the 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 Zenedu. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Zenedu?"
)
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 Zenedu MCP Server
Connect your Zenedu account to any AI agent to automate your messenger marketing and sales funnels. This MCP server enables your agent to interact with bots, products, offers, and subscriber data directly.
LlamaIndex agents combine Zenedu tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the 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
- Bot Oversight — List all your messenger bots and their associated metadata
- Funnel Tracking — List and monitor sales funnels and automation flows for specific bots
- Commerce Management — Access product catalogs and marketing offers synchronized with your bots
- Order Visibility — Retrieve recent customer orders and transaction statuses
- Audience Insight — List and query subscribers to track growth and engagement
The Zenedu MCP Server exposes 6 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 Zenedu to LlamaIndex via MCP
Follow these steps to integrate the Zenedu 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 6 tools from Zenedu
Why Use LlamaIndex with the Zenedu MCP Server
LlamaIndex provides unique advantages when paired with Zenedu through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zenedu tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zenedu tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zenedu, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zenedu tools were called, what data was returned, and how it influenced the final answer
Zenedu + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zenedu MCP Server delivers measurable value.
Hybrid search: combine Zenedu real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zenedu 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 Zenedu for fresh data
Analytical workflows: chain Zenedu queries with LlamaIndex's data connectors to build multi-source analytical reports
Zenedu MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Zenedu to LlamaIndex via MCP:
list_bot_funnels
List all funnels for a specific bot
list_bot_offers
List all offers for a specific bot
list_bot_orders
List all orders for a specific bot
list_bot_products
List all products for a specific bot
list_bot_subscribers
List all subscribers for a specific bot
list_bots
List all messenger bots
Example Prompts for Zenedu in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zenedu immediately.
"List all messenger bots in my Zenedu account."
"Show me the funnels for bot ID '12345'."
"List recent orders for my 'Main Funnel' bot."
Troubleshooting Zenedu MCP Server with LlamaIndex
Common issues when connecting Zenedu to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZenedu + LlamaIndex FAQ
Common questions about integrating Zenedu 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 Zenedu 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 Zenedu to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.