How to Use the ChargeOver MCP in LlamaIndex

Q: Can I use LlamaIndex to search for a specific ChargeOver customer by name?

Yes. You'd have your agent run listbillingcustomers, index the output, and then perform a semantic search on that index. It will return the customer record that best matches your query.

Turn your ChargeOver billing data into a searchable knowledge base with LlamaIndex. Ask questions, get answers from your own financial history.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect ChargeOver MCP to LlamaIndex

Create your Vinkius account to connect ChargeOver 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.

GDPR Free for Subscribers

Setup ChargeOver with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Create a Searchable Customer Index

Use this MCP Server to pull your entire customer list from ChargeOver. LlamaIndex can then ingest the results from `list_billing_customers` and build a vector index from it. Now you can ask natural language questions like "who are our most recent customers?" or "find the customer record for 'ACME Corp'". Your agent queries the index to find the answer, grounded in your actual ChargeOver data.

Ground Your LlamaIndex Agent in Billing Reality

Your agent can use `list_billing_invoices` and `list_billing_transactions` to fetch financial records. LlamaIndex then makes this data part of its knowledge base for Retrieval-Augmented Generation (RAG). When you ask your agent "what was our total transaction volume last month?", it won't hallucinate. It will query the indexed transaction data to give you a precise answer based on the facts it pulled from the ChargeOver MCP Server.

Build Self-Service Billing Portals

You can build a RAG application that lets your customers ask questions about their own billing. The agent can use the ChargeOver tools to fetch a specific user's data, like `list_billing_invoices`, and index it temporarily. The customer can then ask "show me my last invoice" or "when is my next payment due?". The agent provides answers directly from the data it just fetched via the MCP tool, creating a secure, self-service experience.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ChargeOver. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect ChargeOver now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about ChargeOver MCP in LlamaIndex

It uses Retrieval-Augmented Generation (RAG). The agent calls `list_billing_invoices`, indexes the results into a vector store, and then uses that index to find the specific data needed to answer your question.

Yes. You'd have your agent run `list_billing_customers`, index the output, and then perform a semantic search on that index. It will return the customer record that best matches your query.

That's up to you. You control the vector store. You can use an in-memory index for temporary queries or a persistent one to build a long-term knowledge base from your billing history.

Yes, that's what LlamaIndex is built for. You can index your ChargeOver customer list alongside your CRM data or support tickets to create a unified, queryable view of your customers.

The MCP server connection is secured by Vinkius. The data itself—your customer lists, transaction logs, and invoice records—is only handled during the API call. Where you choose to store the indexed data is your decision, but the transport is ephemeral and isolated.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript