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Vinkius runs on Vercel AI SDK

How to Use the Rocket.Chat MCP in Vercel AI SDK

Render Rocket.Chat message histories and directory lookups directly into your React components with this Vercel AI SDK MCP Server.

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Works with every AI agent you already use

…and any MCP-compatible client

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MCP Servers — Included with Plan
Vinkius runs on Vercel AI SDK

Connect Rocket.Chat MCP to Vercel AI SDK

Create your Vinkius account to connect Rocket.Chat to Vercel AI SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Key Capabilities

Real-time UI Updates with Vercel AI SDK

The `chat_post_message` and `chat_update_message` tools let your frontend application post and modify chat text instantly. When an end user triggers an event, your AI client invokes these functions to edit messages in-line without requiring page refreshes. You get immediate feedback loops in your Next.js or React UI because the tool outputs stream straight to the viewport. This bypasses the typical latency of traditional API polling and keeps your workspace communication current.

Live Directory Inspections

The `list_users` and `get_user_info` tools pull raw profile data directly from your chat server. Your application uses these endpoints to look up team members, check statuses, and map internal IDs to real names. Running on Edge Functions ensures these lookups resolve under 50 milliseconds. Developers can build high-speed directory search bars that feed clean JSON directly to the client-side state.

Live Channel Discovery and Auditing

The `list_public_channels` and `list_private_groups` tools expose active discussion spaces for immediate inspection. Your agent scans these collections to find where specific topics are being discussed. Because Vercel AI SDK handles streaming tool calls natively, the list of discovered rooms populates on the screen as the server finds them. This eliminates blank states and keeps users informed during long operations.

Setup guide

Set up Rocket.Chat 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. 1

    Install dependencies

    Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).

  2. 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. 3

    Discover and use tools

    Call mcpClient.tools() to auto-discover all Rocket.Chat tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.

  4. 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 Rocket.Chat transactions",
});

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

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Rocket.Chat. 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.

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 Rocket.Chat MCP in Vercel AI SDK

Install the `@ai-sdk/mcp` package and instantiate the client using the HTTP transport. You pass the server tools directly into the `tools` parameter of `streamText`. Remember to call the close method when your edge function finishes execution.
Yes, the client uses `chat_update_message` to modify any message your bot has permissions to edit. This happens by passing the room ID and message ID directly through the tool call. The updated text renders immediately in the target channel.
The SDK passes execution errors directly to your application logic when limits are hit. You should implement standard exponential backoff in your API route wrappers to handle these exceptions. The underlying MCP Server handles connection pooling to keep overhead low.
Yes, the connection relies on standard HTTP transport which is fully compatible with Edge environments. You do not need heavy node-specific libraries to run these tools. This keeps your serverless cold starts under 100 milliseconds.
This integration processes message text, user profiles, and room IDs entirely within an ephemeral V8 sandbox. No database is attached to the Vinkius MCP gateway, meaning your chat logs never persist on our servers. All credentials travel through encrypted transit layers directly to your workspace.

Start using the Rocket.Chat MCP today

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