How to Use the Unit Economics Calculator MCP in Google ADK

Analyze unit economics profitability using the Google ADK framework on BigQuery data.

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Connect Unit Economics Calculator MCP to Google ADK

Create your Vinkius account to connect Unit Economics 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 Unit Economics Calculator with Google ADK

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Unit Economics Calculator for Google ADK

You already have your customer and revenue history sitting in BigQuery. This MCP lets you analyze it without exporting data first. It calculates the contribution margin, determining gross profit per user by analyzing existing records. It also runs `calculate_payback_period`, telling you exactly how quickly a new customer pays back their cost based on your historical metrics.

Enterprise Financial Modeling via MCP Server

The core function of this server is to give you the LTV:CAC ratio, which is essential for enterprise planning. It uses `rate_health` to grade your unit economics based on those payback periods. Running these checks through Google ADK lets your agents act directly on structured data within the Google Cloud ecosystem.

Long-Context Financial Analysis

Since you're working with massive datasets, this MCP handles the complexity. You pass large chunks of financial data to calculate everything from contribution margin to payback period. It supports long-context reasoning needed for deep dives into complex unit economics structures.

Setup guide

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

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Common questions about Unit Economics Calculator MCP in Google ADK

The MCP toolset allows your agents to execute financial calculations directly using data sources integrated within the Google Cloud environment, like BigQuery.

It needs standard financial metrics, including revenue figures, variable costs, and customer acquisition spend data pulled from your enterprise sources.

Yes. The MCP is designed to work within long-context reasoning models, meaning it can process complex financial inputs efficiently.

You can. By assessing payback periods and running health checks, you determine if your current growth projections are sustainable against variable costs.

It operates on structured financial metrics: revenue amounts, cost inputs for contribution margin calculation, and customer acquisition spend history.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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