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

Q: How do I execute complex SQL logic using Xata (Serverless DB) and Mastra AI?

You use the executesql tool, which runs a single query against your chosen branch. The workflow engine then handles checking the result set for errors or unexpected null values before proceeding to the next step.

Q: Can Mastra AI handle resource limit checks with Xata (Serverless DB)?

Yes. Your agent can call getprojectlimits and include that check in your workflow's conditional branching. If the limits are exceeded, the workflow can trigger an alert or halt gracefully.

Q: How do I list all available organizations with Xata (Serverless DB) and Mastra AI?

The listorganizations tool pulls a record of every organization. Your agent uses this data at the start of a workflow to determine which organizational context it needs to operate in.

Q: What happens if I need to change project details with Xata (Serverless DB) and Mastra AI?

Use updateproject within your workflow. Because the framework supports retries, if the update fails due to temporary network issues, it will automatically attempt the operation again.

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Setup Xata (Serverless DB) with Mastra AI

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Execute Transactions in Batches

The `execute_sql_batch` tool lets your agent run a series of SQL queries as one transaction. This is perfect for workflows where multiple steps must succeed or fail together. The built-in workflow engine handles the result set, ensuring that if any query fails, the entire batch rolls back gracefully.

Manage User Access and Invitations

Need to onboard a user? Use `send_invitation` or `resend_invitation` via the agent. If an invitation fails, the workflow can be configured to automatically retry later with exponential backoff. This is critical for complex multi-step processes that involve external service dependencies.

Update and Maintain Project Metadata

The `update_project` tool lets your agent change project details, like the name or description. This action can be integrated into a workflow triggered by an event. The framework handles this update robustly, allowing you to build automated processes that maintain data integrity over time.

Setup guide

Set up Xata (Serverless DB) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Xata (Serverless DB) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "xata-serverless-db-mcp-client",
  servers: {
    "xata-serverless-db-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Xata (Serverless DB) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Xata (Serverless DB) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Xata (Serverless DB) transactions"
);
console.log(result.text);

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Common questions about Xata (Serverless DB) MCP in Mastra AI

You use the `execute_sql` tool, which runs a single query against your chosen branch. The workflow engine then handles checking the result set for errors or unexpected null values before proceeding to the next step.

Yes. Your agent can call `get_project_limits` and include that check in your workflow's conditional branching. If the limits are exceeded, the workflow can trigger an alert or halt gracefully.

The `list_organizations` tool pulls a record of every organization. Your agent uses this data at the start of a workflow to determine which organizational context it needs to operate in.

Use `update_project` within your workflow. Because the framework supports retries, if the update fails due to temporary network issues, it will automatically attempt the operation again.

This server touches organizational member lists. The framework ensures that every tool invocation requires explicit authentication via an endpoint token, maintaining a secure audit trail for all actions.

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