How to Use the iZooto MCP in LlamaIndex

Q: How do I install the iZooto MCP Server spec in LlamaIndex?

Run pip install llama-index-tools-mcp to get the base adapter. You then initialize BasicMCPClient with your Vinkius URL and convert the server tools into a format your LlamaIndex agent can use.

Q: Can LlamaIndex analyze iZooto campaign performance over time?

Yes, by calling getstats and loading the output into a vector index. This lets you ask natural language questions about click-through rates and subscriber engagement trends.

Q: How does LlamaIndex handle segmentation with iZooto?

The agent uses listsegments to retrieve existing groups, then matches them against your query parameters. If a matching segment does not exist, it calls createsegment to build it.

Q: Does this setup support live push triggers?

Absolutely, you can instruct your agent to call sendpush or sendsegmentpush based on data retrieved during a query pipeline. This turns your search engine into an active execution tool.

Index your web push analytics directly into LlamaIndex to query subscriber trends and campaign performance.

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Connect iZooto MCP to LlamaIndex

Create your Vinkius account to connect iZooto 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 iZooto with LlamaIndex

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Build a searchable push notification index

The `list_notifications` tool retrieves the history of your sent browser alerts. LlamaIndex indexes this text data into a vector store, allowing your agent to run semantic searches over past campaigns to see what messaging angles you have already tried. By combining this historical data with `get_notification` details, your RAG pipeline gets a clear picture of past marketing copy. This prevents your agent from generating repetitive or conflicting messages for current campaigns.

Query iZooto subscriber data via MCP Server

The `get_subscriber_count` tool fetches your current opt-in numbers directly from the API. This MCP server allows LlamaIndex to combine live audience metrics with your internal documents, giving you a unified view of your marketing funnel. Instead of relying on static spreadsheets, your query engine pulls real-time counts alongside segment details from `list_segments`. Your agent can then answer complex questions about user growth trends with perfect accuracy.

Target segments using historical analytics

The `get_stats` tool extracts performance data like click rates and delivery percentages. LlamaIndex stores these metrics, enabling your agent to correlate historical performance with specific audience filters. When you want to run a new campaign, the agent looks up high-performing past segments, calls `create_segment` to build a matching audience, and fires the notification via `send_segment_push`. This ensures your live campaigns are always backed by historical performance data.

Setup guide

Set up iZooto 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 iZooto 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 iZooto tools.",
)
response = await agent.run("List recent iZooto data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by iZooto. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about iZooto MCP in LlamaIndex

Run `pip install llama-index-tools-mcp` to get the base adapter. You then initialize BasicMCPClient with your Vinkius URL and convert the server tools into a format your LlamaIndex agent can use.

Yes, by calling `get_stats` and loading the output into a vector index. This lets you ask natural language questions about click-through rates and subscriber engagement trends.

The agent uses `list_segments` to retrieve existing groups, then matches them against your query parameters. If a matching segment does not exist, it calls `create_segment` to build it.

Absolutely, you can instruct your agent to call `send_push` or `send_segment_push` based on data retrieved during a query pipeline. This turns your search engine into an active execution tool.

The MCP connection runs through a secure, ephemeral sandbox on Vinkius. Your subscriber lists and campaign stats are pulled only when requested, and the raw audience data never leaves your secure environment.

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