How to Use the Construction Cost Estimator MCP in OpenAI Agents SDK

Build production agents that nail construction budget estimates using the OpenAI Agents SDK.

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Connect Construction Cost Estimator MCP to OpenAI Agents SDK

Create your Vinkius account to connect Construction Cost Estimator to OpenAI Agents SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Construction Cost Estimator with OpenAI Agents SDK

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Calculating Foundation Costs with OpenAI Agents SDK

Need to figure out a starting point for any build? You start by calling `get_region_cost_index`. This retrieves the baseline rate for materials and labor in the specified area and month. Once you have that index, your agent passes it along. It uses this base data to feed into `calculate_standard_multiplier`, turning a general standard—like 'mid-grade' or 'premium'—into a hard multiplier factor.

Estimating Final Build Costs with OpenAI Agents SDK

The final step is figuring out the total. Your agent takes the regional index and the calculated standard multiplier, then runs them through `estimate_total_cost_range`. This tool outputs a concrete cost range for the whole project. It’s not just one number; it gives you a defensible minimum and maximum estimate. That immediate data-backed range is what speeds up feasibility checks dramatically.

Building Multi-Step Construction Models with OpenAI Agents SDK

Don't treat these tools as standalone functions. The real power comes from chaining them together. Your agent handles the full flow: first, it gets the region index; next, it computes the standard multiplier; and finally, it generates the total cost estimate. This multi-step process ensures that every variable—from location to material quality—is accounted for in a single workflow managed by your OpenAI Agents SDK.

Setup guide

Set up Construction Cost Estimator 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 Construction Cost Estimator tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Construction Cost Estimator 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="Construction Cost Estimator Agent",
            instructions="You have access to Construction Cost Estimator 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 Construction Cost Estimator MCP in OpenAI Agents SDK

It works by allowing your agent to execute specific, sequential steps. You first grab a regional index using `get_region_cost_index`, then refine that number using `calculate_standard_multiplier` before getting the final range.

Yes. While we recommend current data, the tool supports retrieving baseline rates based on a specified reference month via `get_region_cost_index`. This lets you model costs against past periods.

It handles core geographical and financial data, including area measurements, desired quality standards (like 'premium'), and specific region cost indices. This is all tracked through your agent's audit log.

The tool is designed to provide highly valuable, immediate baseline ranges. You should treat its output as a directional guide, which requires mandatory checks against local expert input.

No. The tool accepts plain parameters—you pass in area measurements and quality descriptions directly to your agent's function calls, making it easy to integrate.

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