How to Use the Meshy (3D AI) MCP in OpenAI Agents SDK

Generate 3D assets in your OpenAI Agents SDK pipeline without leaving your production code.

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Connect Meshy (3D AI) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Meshy (3D AI) 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 Meshy (3D AI) with OpenAI Agents SDK

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Automated 3D asset generation for OpenAI Agents SDK

Trigger `create_text_to_3d_preview` directly from your agent logic to generate geometry from simple prompts. This bypasses the need for manual blocking calls during your production build cycles. Your agents handle the heavy lifting by passing task IDs to `get_text_to_3d_task` to confirm completion. It is a clean way to integrate generative geometry into your existing agent infrastructure.

Refine and rig models inside OpenAI Agents SDK

Use `create_text_to_3d_refine` to polish raw meshes once your agent receives a preview. This ensures your generated assets meet the specific requirements of your project before you commit to final exports. Follow up with `create_rigging` to prepare your models for animation. Your agent manages the entire pipeline, ensuring every asset is ready for your game engine or simulation environment.

Monitor production costs with OpenAI Agents SDK

Keep your budget in check by calling `get_balance` before triggering expensive generative tasks. You can set up your agent to halt execution if your credits drop below a critical threshold. This prevents runaway API costs during high-volume generation runs. Your agent stays within your operational limits by verifying account state before every `create_multi_image_to_3d` request.

Setup guide

Set up Meshy (3D AI) 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 Meshy (3D AI) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Meshy (3D AI) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Meshy (3D AI) 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="Meshy (3D AI) Agent",
            instructions="You have access to Meshy (3D AI) 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 Meshy (3D AI) MCP in OpenAI Agents SDK

Yes. The tools provide direct API access, allowing your agent to manage the full creation lifecycle from preview to final rigging.

Your agent receives the raw response from the server. You can write custom logic to handle retries or fallbacks if a generation task fails.

Yes. Since the MCP server is integrated into the agent constructor, all tool calls are tracked in your observability dashboard.

You can queue multiple tasks by iterating through your generation logic. Each task is tracked individually via its unique ID.

Your prompts and images are sent to the Meshy API for processing. The server uses encrypted connections, and your data remains isolated from other users.

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