How to Use the Cloudflare MCP in Vercel AI SDK

Q: How does the Vercel AI SDK handle long-running Cloudflare deployments?

The SDK uses streamText to surface the progress of createdeployment instantly. Instead of blocking the UI, your agent gets real-time updates as the traffic percentage shifts on the edge.

Q: Can I run Cloudflare D1 SQL queries inside Vercel AI SDK edge runtime?

Yes. Because this MCP Server exposes queryd1 as a standard tool, you can call it directly from edge-compatible routes. The raw JSON output streams straight to your React or Next.js frontend.

Q: How do I manage Cloudflare Worker secrets using Vercel AI SDK?

Your agent can call createsecret or deletesecret dynamically during a chat session. The SDK streams the confirmation back to the user, verifying that the environment variable is injected and ready.

Q: What happens if a Worker route creation fails during an MCP session?

The Vercel AI SDK catches the error thrown by createworkerroute mid-stream. Your agent can immediately read the error and suggest a fix, like checking the zone status first.

Build real-time UIs that deploy Workers and query D1 databases live with the Vercel AI SDK.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Cloudflare MCP to Vercel AI SDK

Create your Vinkius account to connect Cloudflare 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.

GDPR Free for Subscribers

Setup Cloudflare with Vercel AI SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Stream live Worker deployments directly to your Vercel AI SDK UI

Your users can watch code rollouts happen in real-time. By connecting the Vercel AI SDK to this MCP Server, your app calls `create_deployment` and streams the rollout percentage directly to a frontend progress bar. No polling or waiting for a slow API response. If a deploy goes sideways, your AI client can instantly trigger a rollback. It checks the deployment history with `list_deployments` and initiates a gradual traffic shift, updating the user interface as each percentage threshold passes.

Run interactive SQL queries against D1 in your edge functions

Build a database console where your AI agent executes SQL directly inside a Vercel Edge Function. By passing `query_d1` as a tool to `streamText`, the LLM can pull metrics or update records and feed the raw JSON results straight into your React components. Before writing data, the agent can run `list_d1_databases` to pinpoint the exact database ID. This removes the friction of hardcoding IDs in your Next.js configuration, letting the model discover and query the right database on the fly.

Live-stream Worker logs and exceptions to a browser console

Debug production code without leaving your application's UI. This MCP Server lets your Vercel AI SDK agent call `create_tail_session` to open a log stream, piping raw standard output and exceptions directly into a live-updating web terminal. When the developer finishes debugging, the agent automatically runs `delete_tail_session` to clean up. It keeps your active sessions tidy and ensures you aren't paying for unneeded WebSocket connections.

Setup guide

Set up Cloudflare 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 Cloudflare 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 Cloudflare 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 Cloudflare. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Cloudflare now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Cloudflare MCP in Vercel AI SDK

The SDK uses `streamText` to surface the progress of `create_deployment` instantly. Instead of blocking the UI, your agent gets real-time updates as the traffic percentage shifts on the edge.

Yes. Because this MCP Server exposes `query_d1` as a standard tool, you can call it directly from edge-compatible routes. The raw JSON output streams straight to your React or Next.js frontend.

Your agent can call `create_secret` or `delete_secret` dynamically during a chat session. The SDK streams the confirmation back to the user, verifying that the environment variable is injected and ready.

The Vercel AI SDK catches the error thrown by `create_worker_route` mid-stream. Your agent can immediately read the error and suggest a fix, like checking the zone status first.

Your Cloudflare API tokens and Worker secrets never touch Vercel's servers or the client browser. They are isolated in Vinkius's zero-trust MCP sandbox, which injects credentials only at the moment of tool execution and destroys the session immediately after.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript