How to Use the CBRE Econometric Advisors (EA) MCP in OpenAI Agents SDK

Q: How do I connect CBRE Econometric Advisors (EA) to OpenAI Agents SDK?

Install the package via pip install openai-agents. Create an MCPServerStreamableHttp instance using your Vinkius endpoint URL. Pass it to the Agent constructor in the mcpservers array.

Q: Can OpenAI Agents SDK cache the CBRE Econometric Advisors (EA) tool list?

Yes. Set cacheToolsList=True in your configuration. This stops the agent from refetching the schema on every single run, speeding up initialization.

Connect CBRE Econometric Advisors (EA) to OpenAI Agents SDK to pull real estate forecasts directly into production pipelines.

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Connect CBRE Econometric Advisors (EA) MCP to OpenAI Agents SDK

Create your Vinkius account to connect CBRE Econometric Advisors (EA) 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 CBRE Econometric Advisors (EA) with OpenAI Agents SDK

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Pull market forecasts into OpenAI Agents SDK

`get_market_forecasts` gives your agent direct access to historical and projected real estate data. You set the market parameters, and the agent retrieves the exact rent and vacancy timelines needed to build a financial model. Handoffs between specialized agents let one agent gather the raw numbers while another analyzes the trends. Built-in guardrails validate these actions before execution. Your production system confirms the requested market actually exists via `list_cbre_markets` before wasting tokens on a bad API call. Full tracing in the OpenAI dashboard shows exactly how the agent navigated the CBRE EA data structure via the MCP integration.

Evaluate cap rates and NOI metrics

`get_market_cap_rates` pulls current capitalization rates and net operating income figures for any specific market sector. An agent can compare these figures across different regions to spot mispriced assets. The SDK handles the asynchronous requests, letting your system query multiple sectors concurrently. Market context matters when looking at these yields. Your system can run `get_macroeconomic_data` to pull population growth, employment stats, and income levels for the same target area. This combines the real estate performance metrics with the underlying economic drivers that support them.

Map submarkets via MCP Server

`list_asset_sectors` and `list_submarkets` map out the exact hierarchy of the real estate database. Instead of guessing sector names, your agent queries the taxonomy first. This prevents hallucinated asset classes and keeps your data requests strictly within the available categories. Searching for specific indices happens through `search_cbre_assets`. The OpenAI tool auto-discovery mechanism picks up this capability immediately upon connecting the MCP Server. You pass the Vinkius endpoint token, and your agent starts navigating the global real estate hierarchy without manual tool configuration.

Setup guide

Set up CBRE Econometric Advisors (EA) 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 CBRE Econometric Advisors (EA) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives CBRE Econometric Advisors (EA) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate CBRE Econometric Advisors (EA) 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="CBRE Econometric Advisors (EA) Agent",
            instructions="You have access to CBRE Econometric Advisors (EA) 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 CBRE Econometric Advisors (EA) MCP in OpenAI Agents SDK

Install the package via `pip install openai-agents`. Create an `MCPServerStreamableHttp` instance using your Vinkius endpoint URL. Pass it to the Agent constructor in the `mcp_servers` array.

Yes. Set `cacheToolsList=True` in your configuration. This stops the agent from refetching the schema on every single run, speeding up initialization.

The server exposes population, employment, and income data. Your agent can pull these metrics for specific markets to build regional economic profiles.

Auto-discovery handles the tool registration. The agent reads the descriptions for tools like `get_market_forecasts` and decides when to call them based on user prompts.

Vinkius runs this integration inside a V8 Isolate Sandbox. Your real estate market queries, cap rate requests, and macroeconomic indicator parameters never leak across sessions. The zero-trust architecture destroys the environment the moment your script finishes.

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