How to Use the 2Chat MCP in Vercel AI SDK

Stream WhatsApp operations directly into your Next.js frontend with the Vercel AI SDK MCP integration.

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Connect 2Chat MCP to Vercel AI SDK

Create your Vinkius account to connect 2Chat 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 2Chat with Vercel AI SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Live WhatsApp messaging via Vercel AI SDK MCP Server

Your users don't want to stare at loading spinners while your app sends texts. Hook up the `send_message` tool to your Edge Functions and blast out text or media instantly. You get the execution result streamed right back to the UI. Before firing off alerts, your agent checks the connected accounts using `list_numbers`. It picks the correct sender, formats the payload, and dispatches the message. The frontend updates the exact millisecond the API responds.

Filter dead numbers before sending

Bouncing messages tank your sender reputation fast. The `check_number` tool solves this by verifying WhatsApp registration before attempting delivery. Your React component can show a green checkmark the moment validation finishes. Build an intake form where users drop their contact info. Your Vercel AI SDK client validates the input in the background. If the user isn't on WhatsApp, the UI prompts them for an SMS alternative instead.

Spin up chat rooms on demand

Project managers need dedicated channels for new clients right away. This MCP integration pulls the team roster and calls `create_group` to establish a fresh workspace. Everyone gets added instantly. To find an existing thread, the client runs `list_groups` against your connected 2Chat number. It grabs the correct group UUID and drops the onboarding packet right into the chat. No manual admin work required.

Setup guide

Set up 2Chat 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 2Chat 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 2Chat 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 2Chat. 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

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Common questions about 2Chat MCP in Vercel AI SDK

Install @ai-sdk/mcp and set up an HTTP transport. Call createMCPClient with your Vinkius endpoint URL. Pass the resulting tools directly into your streamText function.

Yes. When your agent calls the sending tool, the execution state streams back. You can render a success toast the second the WhatsApp API confirms delivery.

You skip writing boilerplate fetch requests and type definitions. The server hands your LLM exactly what it needs to execute functions autonomously.

The tools handle public URLs perfectly. Just pass a hosted image link into the sending parameters and it drops right into the chat.

Your endpoint token runs inside an ephemeral V8 Isolate Sandbox. We never store the contact lists or message content passing through the tools. The environment self-destructs after the session ends.

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