How to Use the Cooper Hewitt MCP in OpenAI Agents SDK

Build production-ready OpenAI Agents SDK setups that securely query Cooper Hewitt design history with built-in guardrails.

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Connect Cooper Hewitt MCP to OpenAI Agents SDK

Create your Vinkius account to connect Cooper Hewitt 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 Cooper Hewitt with OpenAI Agents SDK

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Validate design history queries via MCP Server

Querying the museum's database starts with `search_objects` to locate specific design artifacts. Your OpenAI Agents SDK setup uses built-in guardrails to validate user inputs before hitting the Cooper Hewitt MCP Server, preventing broken queries or bad API calls. If a user asks for something outside the collection, the agent catches the error early. This keeps your production system clean while fetching actual historical metadata from the Smithsonian database.

Auto-discover exhibition details for multi-agent handoffs

Specialized agents run smoother when they can automatically find tools like `list_exhibitions` and `get_exhibition_info`. This MCP Server exposes museum history directly to your OpenAI Agents SDK pipeline without manual endpoint mapping. One agent can pull the exhibition list while another handles the deep-dive research. The SDK manages these handoffs behind the scenes, making complex curatorial searches highly reliable.

Extract color palettes and media assets safely

Fetching asset visual data is straightforward using `get_object_colors` and `get_object_images` to map design details. OpenAI Agents SDK traces these tool calls in your dashboard so you can see exactly which museum objects are being analyzed. This setup pulls records from the MCP Server cleanly. You get clean color data and image URLs formatted perfectly for downstream processing.

Setup guide

Set up Cooper Hewitt 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 Cooper Hewitt tools at runtime.
3

Create your Agent

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

Run the agent

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

You should configure your agent's retry logic or set cacheToolsList=True during initialization. This reduces the number of schema discovery calls made to the MCP Server, keeping your setup fast and under API limits.

Yes, you can register this server across a multi-agent team. One agent can focus on finding objects using search_collection while a second agent extracts the design details.

It does. When you pass the Vinkius HTTP endpoint to your python script, the SDK automatically registers all 22 MCP Server tools.

The server handles errors gracefully and returns detailed messages. You can use test_error during your local integration tests to verify your agent's error-handling paths.

All requests run inside an ephemeral V8 sandbox on Vinkius, meaning your queries are never stored or used to train models. Only the specific museum search terms and object IDs pass through the proxy to fetch the design records.

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