How to Use the Zenkit MCP in Vercel AI SDK

Q: How do I get list details with Zenkit and Vercel AI SDK?

You call getlistdetails on the MCP Server. This returns all necessary metadata for that specific list, letting your client know exactly what kind of data it's dealing with.

Q: What happens when I use Zenkit and Vercel AI SDK to delete entries?

Calling deleteentry executes the deletion instantly. The MCP Server confirms the action, allowing your client to immediately update its local UI state without needing a full page refresh.

Stream live list and workspace data right into your React, Next.js, or Svelte UI with the Vercel AI SDK.

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Connect Zenkit MCP to Vercel AI SDK

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

Ask AI about this MCP

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Claude

Perplexity

Show Zenkit Data in Real-Time

When you call `list_entries` via the MCP Server, your AI client streams the results directly to the user interface. They see data populating live, ditching the clunky loading spinner. This real-time flow makes building frontend products easier. You're not just getting a JSON blob; you're showing the user *how* the information is assembled.

Manage Core Data Structures

Need to read or write data? The Zenkit MCP Server exposes tools like `create_entry` and `update_entry`. You can build forms that populate a record by calling these functions, all while streaming the results back to your client. It’s straightforward: first, you use `list_workspaces` to find the right container. Then, you execute specific operations on lists within it.

Monitor Your MCP Server Calls

If you're building a complex UI that needs to know what data exists, you can call `list_elements` first. This shows all the fields defined in a list, letting your frontend validate inputs before submission. This gives you perfect control over validation layers. You don’t have to guess what data structure is available; the MCP Server tells you.

Setup guide

Set up Zenkit 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 Zenkit 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 Zenkit transactions",
});

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

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Common questions about Zenkit MCP in Vercel AI SDK

You call `get_list_details` on the MCP Server. This returns all necessary metadata for that specific list, letting your client know exactly what kind of data it's dealing with.

Calling `delete_entry` executes the deletion instantly. The MCP Server confirms the action, allowing your client to immediately update its local UI state without needing a full page refresh.

Absolutely. You start by listing all possible containers using `list_workspaces`. This gives you the scope needed before targeting any specific list or entry data.

This server primarily touches List and Workspace metadata. Specifically, it manages structural information like field definitions (`list_elements`) and records (entries).

Yes, because this server's tools are designed for discrete reads and writes. The architecture supports streaming results cleanly into your React components.

Use it with your favorite AI tools

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

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