How to Use the Unit Economics Calculator MCP in LlamaIndex

Index financial models with LlamaIndex for searchable Unit Economics Calculator insights.

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Connect Unit Economics Calculator MCP to LlamaIndex

Create your Vinkius account to connect Unit Economics Calculator to LlamaIndex — 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 LlamaIndex

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Building RAG Knowledge Bases with LlamaIndex

LlamaIndex lets you index the results of your unit economics analysis so they become part of a searchable knowledge base. When your agent calculates metrics using `calculate_contribution_margin`, that output is saved and indexed. This means you can query past reports or configurations—like 'What was our payback period in Q3?'—and get answers grounded directly in the actual data, not just generalizations.

Semantic Search over Financial Data using LlamaIndex

Don't rely on simple keyword matching. By indexing tool outputs from this MCP Server, you can ask complex questions about your finances. You might ask the index to compare the unit economics health across two different product lines. It merges live API data with documents, creating a unified source of truth for every financial metric calculated.

Unit Economics Calculator Insights in LlamaIndex

You can combine a calculation from this MCP Server, like determining the payback period using `calculate_payback_period`, with internal policy documents. Your agent reads both to give context-aware answers. This is powerful because it grounds advice: if the document says 'low churn is critical,' and your index shows high calculated retention rates, you get a concrete comparison.

Setup guide

Set up Unit Economics Calculator MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Unit Economics Calculator MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Unit Economics Calculator tools.",
)
response = await agent.run("List recent Unit Economics Calculator data")

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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 Unit Economics 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 Unit Economics Calculator MCP in LlamaIndex

Instead of just getting the calculation result, LlamaIndex saves that result as an indexed data point. You can then query historical results or compare different calculated unit economics health scores.

Yes. The live API output from the MCP Server becomes one of the primary data sources for your index, allowing you to combine hard financial metrics with unstructured text data.

It provides contribution margins, calculated payback periods, and overall unit economics health scores. These are all structured, numerical facts perfect for indexing.

The underlying MCP Server uses zero-trust proxies; credentials pass in transit only and never sit on disk. The data you index is treated with the same high security standards.

You can run several calculations, then feed all those outputs into LlamaIndex. It builds a unified context that lets you query relationships between the different calculated numbers.

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