How to Use the Zenedu MCP in LlamaIndex

Q: How does LlamaIndex use Zenedu for RAG?

LlamaIndex indexes the raw output from tools like listbotorders and listbots. When a user queries, the system searches this indexed data first, grounding the answer in real API records rather than just general knowledge.

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Connect Zenedu MCP to LlamaIndex

Create your Vinkius account to connect Zenedu to LlamaIndex 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 Zenedu with LlamaIndex

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Search bot data in a knowledge base

You don't just list bots; you index them. By running `list_bots`, the output becomes part of your vector store, allowing semantic search against all existing messenger bots. Users can ask questions like 'which bot handles billing?' and get an answer grounded in the actual data. This means you query past API results alongside documents, giving true context to your AI application.

Retrieve subscriber history for RAG

Need to know why a user complained? Indexing `list_bot_subscribers` data lets you build a knowledge base around customer profiles. Your agent can then retrieve specific subscriber details when answering a query, referencing actual records instead of guessing. It grounds the AI's response in verified API information.

Query product and funnel structures

The MCP Server lets you index operational data points like `list_bot_funnels` and `list_bot_products`. This means your RAG application can answer questions like 'what are the steps in the premium funnel?' or 'which products belong to bot X?' This capability turns static API calls into dynamic, searchable knowledge.

Setup guide

Set up Zenedu MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Zenedu MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Zenedu tools.",
)
response = await agent.run("List recent Zenedu data")

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Common questions about Zenedu MCP in LlamaIndex

LlamaIndex indexes the raw output from tools like `list_bot_orders` and `list_bots`. When a user queries, the system searches this indexed data first, grounding the answer in real API records rather than just general knowledge.

Zenedu provides communication messaging metrics. The server exposes tools for listing bots, funnels, products, subscribers, and orders, which are all available to be indexed.

Yes. By indexing the results from the MCP Server, you build a persistent knowledge base that lets your AI client recall specific details about bots or subscribers from previous sessions.

The framework supports advanced resource inclusion. You can filter the MCP Server tools and include resources to ensure that only relevant bot data, like specific products or funnels, gets indexed.

Zenedu touches subscriber data related to communication messaging. This includes details about the user's presence in a bot and their associated order history.

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