How to Use the Curve Fitting Engine MCP in LlamaIndex

Index exact regression math into your LlamaIndex vector stores. Turn scatter plot data into queryable mathematical facts.

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Connect Curve Fitting Engine MCP to LlamaIndex

Create your Vinkius account to connect Curve Fitting Engine to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Curve Fitting Engine with LlamaIndex

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Ground LlamaIndex Answers in Exact Math

RAG applications fail when they try to summarize raw numbers. If a user asks for the trendline of last year's sales, reading the raw coordinates from a document won't help. You configure your FunctionAgent to use `calculate_regression` via the MCP Server. The agent reads the scatter plot from your documents, calculates the exact linear equation, and returns an answer based on actual math.

Indexing Polynomial Equations

LlamaIndex lets you store MCP tool outputs directly into your knowledge base. After calculating a complex polynomial fit, that equation becomes a permanent, searchable fact. Future queries about that specific dataset pull the exact R-squared score and coefficients from the vector store. You stop recalculating the same curves and start building a mathematical memory bank.

Zero Hallucination Trendlines

LLMs love to make up numbers that look plausible. This engine stops that completely. By forcing the agent to rely on the deterministic output of the tool, you guarantee the returned slope and intercept are perfectly accurate. The math happens outside the LLM, keeping your RAG pipeline strictly factual.

Setup guide

Set up Curve Fitting Engine 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 Curve Fitting Engine 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 Curve Fitting Engine tools.",
)
response = await agent.run("List recent Curve Fitting Engine data")

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Common questions about Curve Fitting Engine MCP in LlamaIndex

Run pip install llama-index-tools-mcp. Use BasicMCPClient with your endpoint URL, convert it with McpToolSpec, and pass the async tool list to your agent.

Yes. The calculated equations and R-squared scores can be indexed directly. This lets your users query historical regression results without running the math again.

Your FunctionAgent reads the user's prompt. If they ask about trends in a scatter plot, it knows to trigger the tool to find the exact line of best fit.

The agent passes an array of x/y coordinates extracted from your data sources. It specifies whether it needs a linear or polynomial fit.

The Vinkius endpoint operates on a zero-trust model. The raw x/y coordinates you send for regression are processed in memory and immediately destroyed. Zero persistence.

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