How to Use the ChartMogul MCP in AutoGen

Deploy AutoGen agents to analyze ChartMogul revenue data, debate churn risks, and negotiate forecasting models autonomously.

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Connect ChartMogul MCP to AutoGen

Create your Vinkius account to connect ChartMogul 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 ChartMogul with AutoGen

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Resolving MRR disputes with AutoGen

The `get_mrr_metrics` and `get_subscription_metrics` tools feed raw monthly recurring revenue figures to your conversational framework. A financial agent requests the numbers, while a separate risk agent reviews the same output to look for anomalies. These agents debate the implications of the data before returning a final report. If the MRR drops, they negotiate the severity of the trend and agree on a consensus summary for the end user.

Evaluating churn risk collaboratively

`list_mogul_customers` and `get_mogul_customer_details` pull active user rosters and individual billing profiles into the chat context. One agent scans the list for high-value accounts, while another checks their specific subscription status. This creates a dynamic analytical process. Instead of a single script executing commands, competing perspectives evaluate the customer data to flag potential churn risks or upsell opportunities.

Validating billing platforms

`list_mogul_data_sources` and `ping_mogul_api` let your agents verify the ChartMogul configuration before starting their debate. The system confirms authentication and identifies the billing platforms feeding the account. You connect this managed MCP endpoint using streamable HTTP parameters. The framework's adapter automatically handles schema conversion, letting your agents focus entirely on the data analysis.

Setup guide

Set up ChartMogul 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 ChartMogul 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="ChartMogul_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="ChartMogul_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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 ChartMogul. 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 ChartMogul MCP in AutoGen

You install the MCP extension and configure the HTTP server parameters. The adapter converts the tool schemas so your agent can call them natively.

Yes. One agent fetches the MRR or ARR figures, and the entire group can read that JSON output in the shared conversation history.

The agent receives the error message directly in the chat. It can then decide to retry the call, use a different tool, or ask another agent for help.

No. The adapter translates the MCP Server definitions into native function calling formats automatically.

The server processes annualized run rates and individual billing details. Vinkius routes this traffic through stateless, ephemeral sandboxes that process the request and immediately destroy the execution environment.

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