How to Use the BoardMix MCP in Vercel AI SDK

Q: How do I stream BoardMix canvas data using the Vercel AI SDK?

Install @ai-sdk/mcp and initialize the client using createMCPClient with your Vinkius HTTP URL. Pass the result of mcpClient.tools() directly to streamText to let your model fetch nodes. Remember to call mcpClient.close() when the connection finishes to avoid memory leaks.

Q: What is the best way to handle large node lists?

Use listnodes combined with pagination parameters to prevent payload bloat. This approach ensures your MCP Server handles these chunks sequentially, keeping your application responsive and rendering the whiteboard layout progressively.

Feed live BoardMix whiteboard structures directly into your React components in real-time with the Vercel AI SDK.

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Connect BoardMix MCP to Vercel AI SDK

Create your Vinkius account to connect BoardMix to Vercel AI 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 BoardMix with Vercel AI SDK

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Render Live BoardMix Nodes with Vercel AI SDK

`list_nodes` retrieves the raw spatial data of your whiteboard shapes so your application can draw them on the screen immediately. Your Next.js frontend receives this data chunk by chunk, bypassing the usual loading spinners that ruin the user experience. We use the streaming capabilities of this MCP Server to feed coordinate offsets and text fields directly to your custom UI components. Instead of waiting for a massive payload to download, the interface populates individual elements as the model parses them.

Interactive Whiteboard Version Control

`get_file_versions` fetches the history of a visual canvas directly inside your Edge Functions. Because the Vercel AI SDK handles these MCP requests at the edge, your users get sub-millisecond responses when comparing visual changes. You can trigger `get_file` to pull specific historical states without blocking the main browser thread. The integration allows developers to build lightweight visual diff tools that render changes on the fly.

Real-time Comment Overlays

`get_comments` pulls active discussion threads from specific whiteboard coordinates to display them as real-time notifications. Your AI client reads these threads to generate instant contextual summaries for team members. By coupling this tool with `list_files`, the system identifies which canvases need attention without manual user searching. You write simple React hooks that display these comments the moment they are fetched.

Setup guide

Set up BoardMix 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 BoardMix 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 BoardMix transactions",
});

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BoardMix. 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 BoardMix MCP in Vercel AI SDK

Install `@ai-sdk/mcp` and initialize the client using `createMCPClient` with your Vinkius HTTP URL. Pass the result of `mcpClient.tools()` directly to `streamText` to let your model fetch nodes. Remember to call `mcpClient.close()` when the connection finishes to avoid memory leaks.

Yes, this MCP Server operates over standard HTTP, making it fully compatible with Vercel Edge Functions. You do not need heavy node-specific libraries to fetch canvas states. Simply import the SDK and call `list_files` directly from your edge API routes.

Vinkius manages the underlying API tokens, giving you a single secure endpoint. You pass this token via the `authProvider` configuration inside your Vercel AI SDK setup. Your frontend code never exposes raw BoardMix credentials to the browser.

Use `list_nodes` combined with pagination parameters to prevent payload bloat. This approach ensures your MCP Server handles these chunks sequentially, keeping your application responsive and rendering the whiteboard layout progressively.

Vinkius executes all requests inside a zero-trust, ephemeral V8 Isolate Sandbox. Your whiteboard nodes, comments, and project structures are processed in memory and never stored. Once the execution completes, the sandbox is destroyed immediately.

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