How to Use the MindsDB (AI Database & Predictors) MCP in Mastra AI

Build complex predictive workflows in Mastra AI using the MindsDB (AI Database & Predictors) MCP Server.

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Connect MindsDB (AI Database & Predictors) MCP to Mastra AI

Create your Vinkius account to connect MindsDB (AI Database & Predictors) to Mastra AI 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 MindsDB (AI Database & Predictors) with Mastra AI

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Automate predictive workflows in Mastra AI

Chain your logic by using `execute_sql_query` inside your agent steps. If a prediction fails or returns a null result, you can trigger a retry loop immediately. This keeps your data pipeline moving without manual intervention. It’s perfect for multi-step tasks that need to verify model output before moving to the next stage.

Manage your data sources with Mastra AI

Use `list_databases` and `list_views` to map out your infrastructure before running your agents. You’ll know exactly which tables are accessible to your workflow. This discovery phase prevents runtime errors when your agents try to query non-existent schemas. It turns your database into a structured resource for your autonomous agents.

Control tool access with the MindsDB MCP Server

Enable requireToolApproval for critical `execute_sql_query` operations. You can review the exact SQL before the agent runs it against your production data. This adds a layer of safety to your automated tasks. It’s the standard way to ensure your Mastra AI agents don't perform destructive actions without your sign-off.

Setup guide

Set up MindsDB (AI Database & Predictors) 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 MindsDB (AI Database & Predictors) 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: "mindsdb-ai-database-predictors-mcp-client",
  servers: {
    "mindsdb-ai-database-predictors-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "MindsDB (AI Database & Predictors) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to MindsDB (AI Database & Predictors) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent MindsDB (AI Database & Predictors) transactions"
);
console.log(result.text);

Get your connection token →

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

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Common questions about MindsDB (AI Database & Predictors) MCP in Mastra AI

Add the server URL to your MCP client config in Mastra. Then, spread the returned tools into your agent definition to give it full database access.

Mastra handles the workflow logic, including retries. If an `execute_sql_query` call hits a transient error, the agent will catch it and retry based on your policy.

Absolutely. You can define a sequence where the first agent lists models, the second checks status, and the third runs the prediction.

Vinkius manages the connection via an ephemeral sandbox. Your agent only sees what you explicitly allow through the tool filter.

Your training data and query results are processed in an isolated V8 sandbox. We don't store your raw records, keeping your sensitive information confined to your active workflow.

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