How to Use the Jitbit MCP in Vercel AI SDK

Stream Jitbit helpdesk data directly into your Vercel AI SDK interface for real-time ticket management.

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Connect Jitbit MCP to Vercel AI SDK

Create your Vinkius account to connect Jitbit 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 Jitbit with Vercel AI SDK

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Live ticket streams in Vercel AI SDK

Pipe helpdesk data straight into your frontend components. When your agent calls `list_tickets`, the results appear instantly without waiting for a backend refresh. Your users see ticket updates as they happen. It turns a static helpdesk dashboard into a live feed that responds to user input immediately.

Automated ticket creation via MCP Server

Trigger `create_support_ticket` directly from your chat interface. It skips the manual form entry, letting the agent handle the heavy lifting while the user stays in the conversation. This keeps the session flowing. You get the ticket logged in your system while the UI stays focused on the user's request.

Knowledge base access for your agents

Give your agent access to your internal documentation using `list_kb_articles`. It retrieves relevant info before the agent generates a response for the user. Your AI client pulls accurate data from your helpdesk base. It answers questions using your specific company policies rather than generic training data.

Setup guide

Set up Jitbit 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 Jitbit 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 Jitbit transactions",
});

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

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Common questions about Jitbit MCP in Vercel AI SDK

Install the MCP client package and initialize it with your Vercel AI SDK project. You'll pass the tool definitions into your generateText function to start executing commands.

Yes, you can use the tool results to filter your local state. Just pass the output of `list_tickets` into your data processing logic to display only what you need.

The server uses a single endpoint token for authentication. Your data stays encrypted in transit and is only accessed when your agent explicitly triggers a tool.

It does. Because the SDK handles streaming, you can push updates to your UI the moment the server returns a response.

Wrap your tool calls in a standard try-catch block. Since it's just TypeScript, you can handle API failures or empty responses just like any other function.

Use it with your favorite AI tools

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