How to Use the Bridge Data Output MCP in OpenAI Agents SDK

Q: Can I use Bridge Data Output with OpenAI Agents SDK to search properties by price?

Yes, the agent uses the searchpropertiesbyprice tool to query listings matching specific financial criteria. It returns structured JSON payloads directly to your running Python process.

Q: How does Bridge Data Output handle media files in OpenAI Agents SDK?

The listmedia tool returns raw URLs and metadata for photos and videos associated with a listing. Your agent reads these URLs and passes them to vision-capable models for analysis.

Q: Does this MCP Server support real-time agent directories?

It does. The listmembers and listoffices tools let your agent browse active real estate agents and brokerage locations. This is useful for routing leads or verifying agent licensing status.

Fetch raw MLS listings and agent directories directly inside your OpenAI Agents SDK pipeline with strict schema validation.

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Connect Bridge Data Output MCP to OpenAI Agents SDK

Create your Vinkius account to connect Bridge Data Output 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 Bridge Data Output with OpenAI Agents SDK

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Run MLS queries with OpenAI Agents SDK safety

The Bridge Data Output MCP Server exposes raw MLS listings and agent directories directly to your Python agents. You pull real-time property data using `list_properties` and `get_property` without writing custom API wrappers or handling authentication manually. Your agent handles the raw payload and formats it for downstream tasks. OpenAI's native guardrails validate these tool calls before they run, ensuring your production system never makes unauthorized requests to the real estate endpoints.

Map data structures using dataset metadata

Different MLS platforms structure their fields differently, which makes standardization a nightmare. This toolset solves that by exposing `get_dataset_metadata` and `list_data_systems` to inspect target schemas dynamically. Because the SDK caches these tool definitions from the MCP Server, your system resolves schema queries in milliseconds without hitting rate limits.

Build automated real estate valuation pipelines

You can build a multi-agent system where one specialized agent tracks price changes and another pulls media. The agent calls `list_recent_listings` to find new inventory, then hands off the payload to a media-processing agent that calls `list_media` to download high-res photos. The OpenAI dashboard traces this entire handoff, showing you exactly which agent invoked which tool and how much context it consumed. This makes debugging complex multi-agent search workflows straightforward.

Setup guide

Set up Bridge Data Output 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 Bridge Data Output tools at runtime.
3

Create your Agent

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

Run the agent

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

Instantiate MCPServerStreamableHttp with your Vinkius endpoint URL. This registers the MCP tools like `search_properties_by_city` directly with your agent's server list.

Yes, the agent uses the `search_properties_by_price` tool to query listings matching specific financial criteria. It returns structured JSON payloads directly to your running Python process.

The `list_media` tool returns raw URLs and metadata for photos and videos associated with a listing. Your agent reads these URLs and passes them to vision-capable models for analysis.

It does. The `list_members` and `list_offices` tools let your agent browse active real estate agents and brokerage locations. This is useful for routing leads or verifying agent licensing status.

Vinkius runs this MCP Server in an isolated, zero-trust V8 sandbox that destroys itself after execution. Your MLS API keys and property search queries never touch external logs or persistent storage.

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