How to Use the ROAS Calculator MCP in AutoGen

Q: Can I build a robust ROAS Calculator workflow using AutoGen?

Yes. You define the roles, giving them specific tools—like calculatebreakevenroas—and they will work together until they reach a consensus on the final best allocation.

Q: Does AutoGen handle different campaign types with the ROAS Calculator?

It can. You set up agents to run calculateroas for Campaign A, and then immediately run it again for Campaign B, allowing them to debate which channel needs more budget.

Systematic decision-making. AutoGen uses this ROAS Calculator MCP Server to simulate how multiple analysts debate ad budget allocation.

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Create your Vinkius account to connect ROAS Calculator to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup ROAS Calculator with AutoGen

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Debating Ad Performance with the AutoGen Framework

Set up a conversation where one agent runs `calculate_roas` and another challenges it. The third, 'Strategy' agent then uses that output to call `get_target_benchmark`, forcing a discussion on whether current spending is adequate. The agents debate the implications of the benchmark against the actual performance figures.

Consensus-Driven Break-Even Analysis (AutoGen MCP Server)

Build a scenario where one agent calculates the break-even point using `calculate_break_even_roas`. A second agent critiques that number, forcing an iterative refinement of the calculation and resulting in a consensus on minimum viable spend. The outcome is a highly debated, validated financial conclusion.

Multi-Perspective ROAS Auditing with AutoGen

You can define three roles: 'Finance', 'Marketing', and 'Strategy'. The Finance agent runs `calculate_roas`, the Marketing agent critiques the mediaSpend, and the Strategy agent uses that critique to run a final check using the break-even tool. This forces deep analysis where every output is questioned by another specialized agent.

Setup guide

Set up ROAS Calculator 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 ROAS Calculator 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="ROAS Calculator_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent ROAS Calculator 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="ROAS Calculator_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent ROAS Calculator 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 ROAS Calculator. 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 ROAS Calculator MCP in AutoGen

It uses it to simulate conflict. You make agents debate ad spending, forcing them to repeatedly call tools like `calculate_roas` and compare the results against a target benchmark.

It deals with core financial inputs: mediaSpend, sourceRevenue, and contribution margin. These numbers are used as evidence points during the multi-agent debate.

Yes. You define the roles, giving them specific tools—like `calculate_break_even_roas`—and they will work together until they reach a consensus on the final best allocation.

It can. You set up agents to run `calculate_roas` for Campaign A, and then immediately run it again for Campaign B, allowing them to debate which channel needs more budget.

The results are highly vetted. Because multiple agents must agree on the final calculation, you get a consensus that is much harder to challenge than single-source data.

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