ChargeOver MCP Server for LlamaIndexGive LlamaIndex instant access to 7 tools to Create Billing Customer, Create Billing Invoice, Create Subscription, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ChargeOver 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 ChargeOver app connector for LlamaIndex is a standout in the Finance Accounting category — giving your AI agent 7 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 ChargeOver. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in ChargeOver?"
)
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 ChargeOver MCP Server
Connect your ChargeOver account to any AI agent and take full control of your recurring revenue and subscription billing workflows through natural conversation.
LlamaIndex agents combine ChargeOver tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- Billing Profile Orchestration — List and manage customer billing profiles programmatically, retrieving detailed high-fidelity account metadata and payment history
- Subscription Lifecycle Management — Create and update recurring billing packages programmatically to maintain perfectly coordinated subscriber journeys
- Invoice & Statement Architecture — Monitor real-time invoice history and programmatically generate new billing statements to streamline your accounts receivable
- Transaction Intelligence — Track payment transactions, refunds, and credits in real-time to maintain a high-fidelity overview of your financial health
- Operational Monitoring — Access high-level billing summaries and manage account-level metadata directly through your agent for instant financial reporting
The ChargeOver MCP Server exposes 7 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 7 ChargeOver tools available for LlamaIndex
When LlamaIndex connects to ChargeOver through Vinkius, your AI agent gets direct access to every tool listed below — spanning recurring-invoicing, dunning-management, payment-collection, 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.
Create a new customer
Create a new invoice
Create a new subscription
List all customers
List all invoices
List all transactions
List all subscriptions (packages)
Connect ChargeOver to LlamaIndex via MCP
Follow these steps to wire ChargeOver 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 ChargeOver MCP Server
LlamaIndex provides unique advantages when paired with ChargeOver through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ChargeOver tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ChargeOver tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ChargeOver, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ChargeOver tools were called, what data was returned, and how it influenced the final answer
ChargeOver + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ChargeOver MCP Server delivers measurable value.
Hybrid search: combine ChargeOver real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ChargeOver 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 ChargeOver for fresh data
Analytical workflows: chain ChargeOver queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for ChargeOver in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ChargeOver immediately.
"List all active billing customers in ChargeOver."
"Show the last 5 payment transactions and their status."
"Create a new subscription for 'Acme Corp' (ID: '1024') titled 'Pro Plan'."
Troubleshooting ChargeOver MCP Server with LlamaIndex
Common issues when connecting ChargeOver to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpChargeOver + LlamaIndex FAQ
Common questions about integrating ChargeOver MCP Server with LlamaIndex.