How to Use the ROAS Calculator MCP in LlamaIndex

Q: How does LlamaIndex use the ROAS Calculator?

It takes the output of tools like calculateroas and indexes them. This way, ad performance metrics become part of your searchable knowledge base, allowing you to query historical financial results.

Q: Can I ask questions about my ROAS Calculator history with LlamaIndex?

Yes. By indexing the output of calculateroas and gettargetbenchmark, your RAG application can answer specific, data-grounded questions about past ad campaigns.

Q: Does the ROAS Calculator support different business models with LlamaIndex?

Yes. You can run gettargetbenchmark for various industries, and then index both the benchmark data and your own specific performance metrics for comparison later.

Build RAG apps with LlamaIndex. Ground ad spend decisions using the ROAS Calculator MCP Server in a searchable knowledge base.

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Setup ROAS Calculator with LlamaIndex

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Indexing Financial Metrics for LlamaIndex

When you run `calculate_roas`, the resulting metrics—like total mediaSpend and sourceRevenue—can be indexed. This means your RAG application doesn't just answer questions; it grounds answers in actual, calculated financial data. You can query past sessions and get specific ROAS numbers retrieved from the vector store, eliminating guesswork entirely.

Retrieving Break-Even Points with LlamaIndex MCP Server

Need to know what the break-even point was last quarter? Use `calculate_break_even_roas` and index that result. When a user asks, 'What did we need for ROAS last month?', your system retrieves the exact calculated number from the knowledge base. This is how you combine live API data with historical context in one unified search.

Benchmarking Ad Spend for LlamaIndex

Get target ROAS benchmarks via `get_target_benchmark` and store that result. Then, when a user asks about market standards, the system retrieves both the benchmark *and* your own calculated performance using the stored data. This makes your index richer than just documents; it's filled with quantifiable business intelligence.

Setup guide

Set up ROAS 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 ROAS 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 ROAS Calculator tools.",
)
response = await agent.run("List recent ROAS 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 ROAS 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 ROAS Calculator MCP in LlamaIndex

It takes the output of tools like `calculate_roas` and indexes them. This way, ad performance metrics become part of your searchable knowledge base, allowing you to query historical financial results.

Yes. By indexing the output of `calculate_roas` and `get_target_benchmark`, your RAG application can answer specific, data-grounded questions about past ad campaigns.

It handles financial inputs like mediaSpend and sourceRevenue, as well as calculated metrics (ROAS, break-even points) which are then stored in the searchable index.

The calculations are precise because they use defined tools. By indexing them, you ensure that your answers aren't hallucinated; they are traceable back to the calculated source.

Yes. You can run `get_target_benchmark` for various industries, and then index both the benchmark data and your own specific performance metrics for comparison later.

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