How to Use the Xata (Serverless DB) MCP in Vercel AI SDK

Q: How do I check my database structure using Xata (Serverless DB) and Vercel AI SDK?

You use the listbranches tool to see all available branches in a project. Your agent streams this list directly into your UI, letting users select which data set they want to work with immediately.

Q: Can I run batch SQL queries efficiently with Xata (Serverless DB) and Vercel AI SDK?

Yep. The executesqlbatch tool handles running multiple SQL statements in one transaction. Your AI client streams the results back, allowing your frontend to display a consolidated view of all query outputs.

Q: What are the available regions for my database with Xata (Serverless DB) and Vercel AI SDK?

The listregions tool provides a list of available geographic locations. Your agent uses this to populate regional selectors in your application, ensuring users pick a valid deployment spot.

Q: How do I check organization member lists with Xata (Serverless DB) and Vercel AI SDK?

Call listmembers to get all current members associated with an organization. Your agent client streams this array, which you can then display in a user-facing list or table component.

Q: Does the MCP Server handle credentials securely for Vercel AI SDK?

Absolutely. You can use getbranchcredentials to retrieve necessary branch credentials, and since this is managed by an MCP Server, your agent handles the secure transfer of that data.

Stream live database results into your UI with Vercel AI SDK.

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Connect Xata (Serverless DB) MCP to Vercel AI SDK

Create your Vinkius account to connect Xata (Serverless DB) 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 Xata (Serverless DB) with Vercel AI SDK

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Get Live Data Streams for Your Frontend

The `execute_sql` tool lets you run SQL queries against a specific branch. When the data streams back, it updates your UI in real-time, skipping that annoying loading spinner. This is crucial when building user-facing products. You get to show the end user the AI working live as the results stream into React or Next.js components.

Manage Project Resources via MCP Server

Need to check limits before a call? Use `get_project_limits` to read resource constraints for any project. You can then display this data directly in your UI as the user requests it. It’s super useful for client-side checks, allowing you to show users exactly what their current allocation is across different projects.

List Database Branches on Demand

The `list_branches` tool retrieves all available branches within a project. This data can then be immediately displayed in a dropdown or selector component. Your agent client receives this list and streams it, letting your frontend populate the options instantly as the user interacts with the page.

Setup guide

Set up Xata (Serverless DB) 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 Xata (Serverless DB) 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 Xata (Serverless DB) transactions",
});

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

Get your connection token →

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Common questions about Xata (Serverless DB) MCP in Vercel AI SDK

You use the `list_branches` tool to see all available branches in a project. Your agent streams this list directly into your UI, letting users select which data set they want to work with immediately.

Yep. The `execute_sql_batch` tool handles running multiple SQL statements in one transaction. Your AI client streams the results back, allowing your frontend to display a consolidated view of all query outputs.

The `list_regions` tool provides a list of available geographic locations. Your agent uses this to populate regional selectors in your application, ensuring users pick a valid deployment spot.

Call `list_members` to get all current members associated with an organization. Your agent client streams this array, which you can then display in a user-facing list or table component.

Absolutely. You can use `get_branch_credentials` to retrieve necessary branch credentials, and since this is managed by an MCP Server, your agent handles the secure transfer of that data.

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