How to Use the Lanhu MCP in OpenAI Agents SDK

Connect Lanhu to OpenAI Agents SDK to automate design handoffs and sync asset metadata directly into your production agent pipeline.

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Connect Lanhu MCP to OpenAI Agents SDK

Create your Vinkius account to connect Lanhu 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 Lanhu with OpenAI Agents SDK

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Automate design asset retrieval

Your agent fetches design files and board structures instantly. Use `get_file` and `list_boards` to pull the latest versions into your build environment without manual exports. This keeps your repository in sync with the design team's latest pushes. Your agent handles the heavy lifting so your codebase reflects current UI specs.

Audit design layers with OpenAI Agents SDK

Expose file architecture by calling `list_layers` directly from your agent logic. This lets your code inspect visual properties and metadata before triggering any downstream tasks. It removes the guesswork from component mapping. Your agent validates the design state before proceeding with any automated code generation.

Sync team feedback loops

Pull comments and member lists using `get_comments` and `list_members`. Your agent can now track design discussions and assign follow-up tasks based on real-time feedback. Don't let design requests get lost in threads. This MCP server gives your agent the visibility to act on feedback as soon as it hits the board.

Setup guide

Set up Lanhu 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 Lanhu tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the necessary packages and point your agent to the Vinkius endpoint. Use the `MCPServerStreamableHttp` class to register the server and let the agent discover the tools automatically.

Yes, but keep an eye on your payload sizes. While the tools handle standard design exports, massive files can hit memory limits if you aren't careful with how you cache the data.

Your data stays private. The server only touches the specific design assets and comments you request. Vinkius keeps the connection secure through ephemeral tokens.

The server returns a standard error message detailing exactly what went wrong. You can catch these exceptions in your agent code to retry or log the failure.

You have full control over which tools your agent sees. Simply restrict the tool list in your agent configuration to limit access to specific Lanhu functionality.

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