Upzelo MCP Server for LlamaIndexGive LlamaIndex instant access to 10 tools to Get Customer, Get Flow, Get Flow Session, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Upzelo 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 Upzelo app connector for LlamaIndex is a standout in the Sales Automation 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 Upzelo. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Upzelo?"
)
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 Upzelo MCP Server
Connect your Upzelo churn management account to any AI agent and simplify how you retain customers and manage subscription lifecycles through natural conversation.
LlamaIndex agents combine Upzelo 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
- Customer Management — List and search customer records, and update profile data for better segmentation and targeting.
- Retention Flows — List available flows and manually trigger retention sequences for customers at risk of cancelling.
- Subscription Tracking — Query all tracked subscriptions and update statuses or trial details programmatically.
- Flow Monitoring — Check the real-time status and outcomes of active flow sessions to verify retention success.
- External ID Sync — Link your internal system identifiers to Upzelo customer records for seamless integration.
The Upzelo 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 Upzelo tools available for LlamaIndex
When LlamaIndex connects to Upzelo through Vinkius, your AI agent gets direct access to every tool listed below — spanning churn-reduction, subscription-management, customer-retention, 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 details for a specific customer
Get details for a specific flow
Check the status of a flow session
Get details for a specific subscription
List all customers in Upzelo
List all retention flows
List all subscriptions
Used for segmentation and targeting. Create or update a customer record
Initialize a flow for a customer
Update subscription attributes
Connect Upzelo to LlamaIndex via MCP
Follow these steps to wire Upzelo 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 Upzelo MCP Server
LlamaIndex provides unique advantages when paired with Upzelo through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Upzelo tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Upzelo tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Upzelo, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Upzelo tools were called, what data was returned, and how it influenced the final answer
Upzelo + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Upzelo MCP Server delivers measurable value.
Hybrid search: combine Upzelo real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Upzelo 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 Upzelo for fresh data
Analytical workflows: chain Upzelo queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Upzelo in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Upzelo immediately.
"List all customers currently tracked in Upzelo."
"Trigger the 'Basic Retention' flow (ID: fl_8823) for customer 'cust_1029'."
"Show me the details for subscription 'sub_12903'."
Troubleshooting Upzelo MCP Server with LlamaIndex
Common issues when connecting Upzelo to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpUpzelo + LlamaIndex FAQ
Common questions about integrating Upzelo MCP Server with LlamaIndex.