How to Use the ChargeOver MCP in AutoGen

Let your AutoGen agents collaborate and debate on ChargeOver billing tasks. Create invoices and subscriptions through team consensus.

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Connect ChargeOver MCP to AutoGen

Create your Vinkius account to connect ChargeOver to AutoGen 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 ChargeOver with AutoGen

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Create Invoices with Agent Oversight

With AutoGen, creating an invoice isn't a single command—it's a conversation. A "BillingClerk" agent can propose an invoice by preparing the arguments for the `create_billing_invoice` tool. Before execution, a "Compliance" agent can step in, use `list_billing_customers` to verify the customer's status, check against internal rules, and give its approval. This is how you build reliable automations with an MCP Server.

Debate New Subscription Plans

Have a team of agents manage your subscription offerings. One agent might suggest creating a new package using `create_subscription`. Another agent can act as a "Finance" expert, pulling data with `list_subscriptions` and `list_billing_transactions` to analyze the potential impact. They can debate pricing and features before the final command is ever sent to ChargeOver. This MCP Server gives them the data they need to have an informed discussion.

Your Own Automated Billing Team

Set up a group chat with specialized agents. A "CustomerService" agent can use `create_billing_customer`. An "AccountsReceivable" agent can use `list_billing_invoices` to track payments. A "Manager" agent oversees their work and gives the final sign-off. They work together, using the ChargeOver tools as their shared interface to the billing system. AutoGen orchestrates the conversation, ensuring tasks are done correctly and with proper checks.

Setup guide

Set up ChargeOver MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes ChargeOver tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="ChargeOver_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent ChargeOver data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="ChargeOver_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent ChargeOver data")
    print(result.messages[-1].content)

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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 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.

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Common questions about ChargeOver MCP in AutoGen

You design a multi-agent workflow. One agent suggests calling `create_billing_invoice`, and another agent's job is to review the proposal, perhaps by using `list_billing_customers` to check details, before giving a "go" or "no-go" in the chat.

Yes, that's a perfect use case. You can have a "Sales" agent call `create_billing_customer`, and upon its success, an "Onboarding" agent automatically picks up the task to call `create_subscription`.

It's like a chat log. You'll see one agent say, "I plan to create an invoice for customer X," and another might reply, "Hold on, let me check their transaction history first with `list_billing_transactions`." They reason and act based on that shared information.

Definitely. You can create a "silent" agent in the group chat whose only job is to use tools like `list_billing_transactions` and `list_subscriptions` to report on changes or flag anomalies, without directly creating anything.

The data is exposed only within the agent conversation you've configured. The MCP server itself processes requests in a zero-trust, ephemeral sandbox. Your customer, subscription, and invoice details are not logged or stored by the Vinkius platform.

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