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Vercel Connect now supports TanStack AI

Content Engineer

Agents built with TanStack AI can now call OAuth-protected MCP servers through Vercel Connect, with no credentials for you to store or rotate.

The new @vercel/connect/tanstack-ai subpath exports connectMCPTransport, which takes a TanStack transport config and attaches a Connect-backed auth provider. The provider is called before every MCP request, so the token is always fresh.

import { chat, toServerSentEventsResponse } from '@tanstack/ai';
import { createMCPClient } from '@tanstack/ai-mcp';
import { vercelGatewayText } from '@tanstack/ai-vercel-gateway';
import {
  connectMCPTransport,
  getConsentChallenge,
} from '@vercel/connect/tanstack-ai';


export async function POST(request: Request) {


  const { messages } = await request.json();
  const userId = await getUserId(request);


  try {


    const linear = await createMCPClient({
      transport: connectMCPTransport(
        { type: 'http', url: 'https://mcp.linear.app/mcp' },
        'oauth/linear',
        { subject: { type: 'user', id: userId } },
      ),
    });


    const stream = chat({
      adapter: vercelGatewayText('anthropic/claude-opus-5'),
      messages,
      mcp: { clients: [linear] },
    });
    return toServerSentEventsResponse(stream);


  } catch (err) {


    const challenge = getConsentChallenge(err);
    if (challenge) return Response.redirect(challenge.url, 303);
    throw err;


  }
}

Connect a Linear MCP client with a per-user token and redirect to consent when needed.

If the user has not granted access, createMCPClient fails with a consent challenge before the model runs. Catch it with getConsentChallenge and redirect to Connect's consent URL. Otherwise, a consent error raised within a tool call would reach the model as an error string rather than the user as a redirect.

Read the TanStack AI integration docs to get started, or see the full list of supported frameworks and adapters for Vercel Connect.

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