How to Use the Upzelo MCP in LlamaIndex

Q: How can I use Upzelo and LlamaIndex together?

You index the results from tools like listcustomers into a vector store. This lets your agent answer questions based on actual Upzelo data, not just general knowledge.

Q: Does Upzelo support segmentation indexing with LlamaIndex?

Yes. By calling savecustomer, you generate structured segment data that can be indexed. This allows you to query segments historically for better targeting.

Q: How do I query past retention efforts using Upzelo and LlamaIndex?

You index the output of getflowsession. You can then ask natural language questions that pull up the exact status and details from past campaign runs.

Q: Can I track subscription changes with Upzelo and LlamaIndex?

Definitely. By indexing data from getsubscription, your system maintains a searchable history of when and how subscription attributes were updated.

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Connect Upzelo MCP to LlamaIndex

Create your Vinkius account to connect Upzelo 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 Upzelo with LlamaIndex

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Historical Customer Deep Dives

Instead of guessing, you index the results. Querying past sessions allows your application to pull up specific customer data by running `get_customer` and storing that output. Later, you can ask questions like, 'What was the last recorded subscription status for this account?' The answer comes from the indexed API result.

Flow Session Auditing

Need to know why a previous retention effort failed? You run `get_flow_session` and index that output. Your knowledge base then answers complex questions like, 'What was the status of flow XYZ last Tuesday?' This gives you ground truth over pure hallucination.

Subscription Change Tracking

LlamaIndex can combine document data with live API results. If a customer complains about billing, your agent combines that text prompt with indexed historical `get_subscription` outputs. It answers not just *what* the current status is, but *why* it changed.

Setup guide

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

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Common questions about Upzelo MCP in LlamaIndex

You index the results from tools like `list_customers` into a vector store. This lets your agent answer questions based on actual Upzelo data, not just general knowledge.

Yes. By calling `save_customer`, you generate structured segment data that can be indexed. This allows you to query segments historically for better targeting.

This server manages customer records, flow configurations, and subscription details. Everything returned is structured and ready for indexing.

You index the output of `get_flow_session`. You can then ask natural language questions that pull up the exact status and details from past campaign runs.

Definitely. By indexing data from `get_subscription`, your system maintains a searchable history of when and how subscription attributes were updated.

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