Vercel Connect now supports TanStack AI
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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