How to Use the Curve Fitting Engine MCP in OpenAI Agents SDK

Q: How do I set up the Curve Fitting Engine with the OpenAI Agents SDK?

You'll pip install openai-agents and then instantiate an MCPServerStreamableHttp object with your Vinkius endpoint URL. Pass that into the Agent constructor in the mcpservers list. The agent discovers the tools automatically.

Run deterministic curve fitting inside your production OpenAI Agents SDK system. Get exact math, not approximations.

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Connect Curve Fitting Engine MCP to OpenAI Agents SDK

Create your Vinkius account to connect Curve Fitting Engine to OpenAI Agents SDK 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 OpenAI Agents SDK

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Exact Regression, Not Guesswork

This server gives your agent one tool: `calculate_regression`. You feed it raw scatter plot data, and it returns the exact coefficients, equation, and R-squared value for a linear or polynomial fit. It's not an LLM approximation; it's a direct mathematical calculation. The OpenAI Agents SDK handles the structured data automatically. Your agent gets back clean, predictable results it can immediately use for analysis or reporting. No parsing strings, no guesswork.

Agent Guardrails for Safer Math

Before your agent even calls the `calculate_regression` tool, the SDK's built-in guardrails can validate the input. You can set rules to ensure the dataset isn't empty or malformed, preventing wasted calls and errors. This is critical for production systems. It means you can let agents work with user-provided data more safely. The tool does its job, but your guardrails make sure it's only doing it on good data.

Specialist Agents and MCP Server Tracing

Designate a specific agent as your math specialist. A data-gathering agent can collect information and then hand off the dataset to the math agent for analysis using this MCP Server. This keeps your agent roles clean and focused. Every call is fully traced in the OpenAI dashboard. You can see exactly which agent called `calculate_regression`, what data it sent, and what equation it got back. It makes debugging complex multi-agent workflows straightforward.

Setup guide

Set up Curve Fitting Engine MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Curve Fitting Engine tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Curve Fitting Engine tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Curve Fitting Engine tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Curve Fitting Engine Agent",
            instructions="You have access to Curve Fitting Engine tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Curve Fitting Engine MCP in OpenAI Agents SDK

You'll pip install `openai-agents` and then instantiate an `MCPServerStreamableHttp` object with your Vinkius endpoint URL. Pass that into the `Agent` constructor in the `mcp_servers` list. The agent discovers the tools automatically.

It performs exact deterministic fitting for Linear and Polynomial regressions. You provide the scatter plot data, and it returns the mathematically correct coefficients, equation, and R-squared score.

Yes. This is a common pattern. You can have one agent collect data and then hand it off to a specialized 'math agent' that has access to this MCP Server to perform the calculation.

The R-squared value measures how well the calculated regression line fits your data, on a scale from 0 to 1. An agent can use this score to decide if the trend it found is statistically significant or just noise.

Your scatter plot data is processed in an ephemeral, sandboxed Vinkius environment. The server that performs the calculation has no disk, and its memory is wiped after your request is complete. It's a zero-trust execution model.

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