How to Use the LTV:CAC Calculator MCP in Google ADK

Analyze profitability with Google ADK. LTV:CAC Calculator built for enterprise data sets.

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Connect LTV:CAC Calculator MCP to Google ADK

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

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Setup LTV:CAC Calculator with Google ADK

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Run the MCP Server on Google ADK

Use this toolset to run `evaluate_profitability` against your existing BigQuery data. The agent evaluates the LTV:CAC ratio, giving you a clear payback period and profitability verdict. Since it integrates with Vertex AI, you can build complex pipelines that pass historical financial records directly into the calculator.

Calculate Customer Acquisition Costs

`calculate_cac` accepts structured data from your Google Cloud sources. You just feed in JSON arrays detailing channel spend and new customers acquired to get a per-channel cost breakdown. This makes sense for large enterprise deployments where you manage dozens of acquisition channels.

Determine Customer Lifetime Value

Model LTV using `calculate_ltv`. By combining ARPU, gross margin percentage, and churn rate—metrics likely already in BigQuery—the agent determines the long-term value of a user. This process is reliable for large-scale analysis that requires high context memory.

Setup guide

Set up LTV:CAC 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 LTV:CAC 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="LTV:CAC Calculator_agent",
    model="gemini-2.0-flash",
    instruction="You have access to LTV:CAC Calculator tools via MCP.",
    tools=mcp_tools,
)

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Common questions about LTV:CAC Calculator MCP in Google ADK

The agent treats this MCP like any other data source. It calls the tools and uses Gemini's long-context reasoning to analyze the resulting ratio alongside massive datasets in BigQuery.

The tool needs clear financial inputs: spending totals, customer counts, average revenue per user (ARPU), and retention rates.

Yes. You can set up a chain where initial CAC data triggers an LTV calculation, allowing you to test hypotheses about market changes before committing resources.

It processes numerical financial metrics—costs, revenues, percentages, and counts. It's purely quantitative analysis.

Instead of just listing costs in a report, your agent runs the calculation to generate an actionable verdict and payback period, giving immediate strategic direction.

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