How to Use the Lanhu MCP in Vercel AI SDK

Q: How do I stream Lanhu asset metadata with Vercel AI SDK?

Use listprojectfiles inside your Vercel AI SDK streaming text generation loop to fetch Lanhu metadata. This feeds the asset paths directly to your React components as they render. It avoids blocking the Next.js main thread during heavy design parses.

Q: Can Vercel AI SDK fetch specific Lanhu layers directly?

Yes, your Vercel AI SDK client calls listlayers to pull the exact Lanhu vector and text nodes. It then maps these nodes to your React components in real-time. This eliminates the need for manual design-to-code translations.

Q: How do I configure the Lanhu MCP Server within Vercel AI SDK?

Instantiate the Lanhu MCP Server using createMCPClient and pass the tools to your Vercel AI SDK streamText function. Always call mcpClient.close() when the stream finishes to prevent memory leaks in your Next.js runtime.

Q: How do we handle large Lanhu design files in our React UI?

Run listboards first within Vercel AI SDK to get a lightweight list of layout boards. Avoid calling getfile on the entire Lanhu project at once to prevent blocking your React rendering performance.

Pipe Lanhu design files and comments directly into your React components in real time using the Vercel AI SDK and the Lanhu MCP Server.

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Setup Lanhu with Vercel AI SDK

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Render Lanhu layers live in Vercel AI SDK

`list_layers` retrieves the Lanhu design hierarchy so your Vercel AI SDK client can stream the exact structure directly into your frontend code. It bypasses manual downloads by feeding Lanhu layer specs straight into your Vercel AI SDK UI generation components. Your Next.js users watch the layout build out step-by-step as Vercel AI SDK uses `get_file` to fetch Lanhu visual assets in the background. No loading spinners, just raw layout data turning into React code before their eyes.

Sync Lanhu feedback using this MCP Server

`get_comments` extracts active design feedback from Lanhu directly into your Vercel AI SDK chat interface using this MCP Server. This prevents developers from having to leave their coding sandbox to read designer notes. We use `list_members` to map Lanhu comments to the right team members instantly within the Vercel AI SDK interface. By linking conversations directly to code changes, you cut out manual coordination.

Browse Lanhu files from Vercel AI SDK

`list_project_files` allows your Vercel AI SDK application to query available Lanhu design assets across your workspace. You can filter by project ID to pull the exact Sketch or Figma imports you need in your Next.js app. The Vercel AI SDK client runs `list_boards` to let users select specific Lanhu artboards directly from your custom React dashboard. It keeps your development team aligned with the latest design updates without manual exports.

Setup guide

Set up Lanhu MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Lanhu tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Lanhu transactions",
});

console.log(text);
await mcpClient.close();

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lanhu. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Lanhu MCP in Vercel AI SDK

Use `list_project_files` inside your Vercel AI SDK streaming text generation loop to fetch Lanhu metadata. This feeds the asset paths directly to your React components as they render. It avoids blocking the Next.js main thread during heavy design parses.

Yes, your Vercel AI SDK client calls `list_layers` to pull the exact Lanhu vector and text nodes. It then maps these nodes to your React components in real-time. This eliminates the need for manual design-to-code translations.

Instantiate the Lanhu MCP Server using `createMCPClient` and pass the tools to your Vercel AI SDK `streamText` function. Always call `mcpClient.close()` when the stream finishes to prevent memory leaks in your Next.js runtime.

Run `list_boards` first within Vercel AI SDK to get a lightweight list of layout boards. Avoid calling `get_file` on the entire Lanhu project at once to prevent blocking your React rendering performance.

Your Lanhu design files, layer text, and team comments remain in a secure V8 sandbox. This MCP Server ensures that layout structures are processed entirely in memory and never stored on external servers. All data passes through ephemeral, zero-trust endpoints directly to your Vercel AI SDK client.

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