How to Use the ROAS Calculator MCP in Google ADK

Q: Can the ROAS Calculator help me model spending with Google ADK?

Absolutely. Use calculatebreakevenroas to establish minimum thresholds. This prevents overspending and gives you a safe starting point for any new ad campaign.

Q: What is the target ROAS benchmark using the ROAS Calculator with Google ADK?

The gettargetbenchmark tool pulls industry averages, allowing your agent to compare its findings against best practices. This helps validate if the performance data from BigQuery makes sense.

Run deep ROAS analysis on your Google ADK enterprise data sets.

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Connect ROAS Calculator MCP to Google ADK

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Setup ROAS Calculator with Google ADK

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Calculate ad performance using the MCP Server.

The `calculate_roas` tool processes massive datasets, taking media spend and source revenue to determine a precise Return on Ad Spend. This is ideal if your data already lives in BigQuery. You pass structured data directly from your Google Cloud environment into this MCP for immediate calculation.

Plan budgets with the ROAS Calculator using Google ADK.

Before running any campaign, you need to know the risk. Use `get_target_benchmark` to fetch industry-standard performance goals for your sector. This ensures your agents operate with realistic expectations. Then, run `calculate_break_even_roas`. You'll get a concrete number showing exactly what ROAS you must hit just to break even.

Automate financial reporting on the MCP Server.

Your agent can build full ad strategy reports. It starts by figuring out required minimums with `calculate_break_even_roas`. Next, it calculates current performance using `calculate_roas`, and finally, it benchmarks that result against market standards via `get_target_benchmark`. This means your Google ADK agent can deliver complete financial analysis without leaving the Google Cloud ecosystem.

Setup guide

Set up ROAS Calculator MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with ROAS Calculator tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="ROAS Calculator_agent",
    model="gemini-2.0-flash",
    instruction="You have access to ROAS Calculator tools via MCP.",
    tools=mcp_tools,
)

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Common questions about ROAS Calculator MCP in Google ADK

You feed the service media spend and source revenue, using the `calculate_roas` tool. The result is a single, clear metric showing your current ad return.

Absolutely. Use `calculate_break_even_roas` to establish minimum thresholds. This prevents overspending and gives you a safe starting point for any new ad campaign.

The `get_target_benchmark` tool pulls industry averages, allowing your agent to compare its findings against best practices. This helps validate if the performance data from BigQuery makes sense.

Yes. You can chain tools together: first, find the target benchmark; then, use `calculate_roas` to check current performance against that goal.

This MCP processes financial metrics including media spend, source revenue, and contribution margins. It requires structured ad performance data for calculation.

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