How to Use the LTV:CAC Calculator MCP in Pydantic AI

Get guaranteed financial correctness. LTV:CAC Calculator for Pydantic AI.

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Connect LTV:CAC Calculator MCP to Pydantic AI

Create your Vinkius account to connect LTV:CAC Calculator to Pydantic AI — 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 Pydantic AI

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Validate Profitability with the MCP Server

The `evaluate_profitability` tool handles complex inputs and validates the final output against a strict schema. It provides LTV:CAC ratio, payback period, and verdict—all guaranteed correct. This is perfect for critical financial workflows where silent data corruption means nothing.

Calculate Customer Acquisition Costs

Use `calculate_cac` to process channel spend records. You pass in a JSON array of {channelName, totalSpend, newCustomersAcquired}. The Pydantic framework ensures the resulting CAC per channel is structured correctly. No unexpected fields means no wasted time debugging your agent's output.

Determine Customer Lifetime Value

`calculate_ltv` takes ARPU, gross margin percentage, and churn rate. The runtime validation ensures that the formula is applied correctly based on defined Pydantic models for maximum trust. Correctness over speed—that's what this toolset guarantees.

Setup guide

Set up LTV:CAC Calculator MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "ltvcac-calculator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to LTV:CAC Calculator tools.",
)

result = await agent.run("List recent LTV:CAC Calculator transactions")
print(result.output)

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Common questions about LTV:CAC Calculator MCP in Pydantic AI

The framework validates the input and output of every tool call. If the calculator returns unexpected data, your agent fails loudly with a specific validation error, never silently corrupting your results.

It uses standard quantitative inputs: costs, revenues, rates (ARPU, churn), and discrete customer counts. The focus is on clean numerical metrics.

Yes. You can chain multiple calls—for example, running CAC first, then passing that validated output to calculate LTV—knowing every step's result is type-safe and reliable.

It touches financial metrics: costs (floats), counts (integers), rates (percentages), and descriptive channel names (strings).

Because every tool call is validated, you get a clean, structured audit trail of exactly what inputs were used to derive the final ratio and verdict.

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