How to Use the Blended CAC Calculator MCP in Cursor

Q: How do I use Blended CAC Calculator with Cursor to write a cost-aware feature?

You let the agent call assessbudgetefficiency. It returns ranked channels and percentages, which you can immediately inject into your code. This ensures your new feature uses optimized financial assumptions.

Q: Can I use Cursor to check if my current CAC trend is sustainable?

Yes. By calling analyzemomtrend, the agent tells you the M-o-M shift in CAC. You can write code that flags any significant positive trend, signaling a problem.

Q: If I need historical blended CAC analysis in Cursor, how do I proceed?

You first use calculatecacmetrics to get the baseline figures. Then you feed those results into a trend checker like analyzemomtrend to see the pattern.

Inject live financial metrics into your code using Blended CAC Calculator in Cursor.

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Setup Blended CAC Calculator with Cursor

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Validate cost assumptions for new features.

Before you write a line of business logic, check the numbers. `analyze_mom_trend` takes spending data and tells your code if the CAC trend is positive (bad) or negative (good). You can build checks right into your codebase. This lets you validate assumptions against real-world cost changes. If the historical data shows a sudden dip, you know to flag it in your feature implementation.

Generate optimal resource allocation logic.

Writing budget distribution code? Use `assess_budget_efficiency`. Feed it period spending data and the agent returns not just ranked channels, but suggested percentages. You can then use that output to write your allocation function. The result is clean JSON containing channel names, spend amounts, and calculated CACs—perfect for direct insertion into a service layer.

Calculate the baseline blended CAC metric.

Need the starting point for a model? `calculate_cac_metrics` accepts raw spending data and figures out both per-channel and overall blended CAC. This is your core financial input. This function generates the structured JSON array of `{channelName, spendAmount}` objects that most internal services require.

Setup guide

Set up Blended CAC Calculator MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Blended CAC Calculator MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Blended CAC Calculator transactions." If the MCP tools are loaded correctly, Cursor will call the Blended CAC Calculator tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "blended-cac-calculator-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Blended CAC 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 Blended CAC Calculator MCP in Cursor

You let the agent call `assess_budget_efficiency`. It returns ranked channels and percentages, which you can immediately inject into your code. This ensures your new feature uses optimized financial assumptions.

Yes. By calling `analyze_mom_trend`, the agent tells you the M-o-M shift in CAC. You can write code that flags any significant positive trend, signaling a problem.

For basic metrics, it needs a list of channels and their associated spending amounts for the period. This simple input allows you to generate both per-channel and blended CAC figures.

The MCP is designed to handle standard financial metrics like spend amount and channel name, providing reliable JSON outputs that your code can consume directly without manual parsing.

You first use `calculate_cac_metrics` to get the baseline figures. Then you feed those results into a trend checker like `analyze_mom_trend` to see the pattern.

Use it with your favorite AI tools

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