How to Use the Databox MCP in Mastra AI

Build automated analytics workflows in Mastra AI that handle Databox data operations with conditional retry logic.

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Connect Databox MCP to Mastra AI

Create your Vinkius account to connect Databox 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 Databox with Mastra AI

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Automated Databox workflows in Mastra AI

Use `list_datasets` and `list_data_sources` to trigger complex agent logic based on your current account status. If a data source is missing, your agent can automatically call `create_data_source` to fix the gap. This eliminates manual intervention for common setup tasks. Your agents now handle the boring parts of maintaining your analytics pipeline.

Conditional data logging with MCP Server

Set up retry logic for `push_metrics_data` within your Mastra workflows. If the ingest fails, the system automatically attempts to send the data again without stopping your agent. This makes your data pipeline resilient to network hiccups. You get reliable metric ingestion without writing hundreds of lines of error-handling code.

Audit Databox activity in Mastra AI

Check your API history using `list_activity_logs` to see exactly what your agents are doing. This gives you a clear audit trail for every action performed on your account. Reviewing these logs helps you tune your agent behavior. You can see when tools were called and verify that your automation logic is firing correctly.

Setup guide

Set up Databox 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 Databox 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: "databox-mcp-client",
  servers: {
    "databox-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Why Choose Vinkius

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Common questions about Databox MCP in Mastra AI

Install the Mastra MCP package and initialize the client using your Vinkius URL. Spread the tools from the server into your agent definition to give it access to your metrics.

Yes, use the requireToolApproval setting in your agent configuration. This forces the agent to pause before executing destructive actions like deleting a dataset.

Definitely. You can build a workflow that lists metrics, performs calculations, and pushes the results back to Databox as a new dataset.

Mastra handles retries automatically when you configure the workflow engine. If the tool fails, the agent waits and tries again based on your specified backoff settings.

The Vinkius sandbox ensures your credentials are never exposed to the agent framework. Only the specific tool outputs are visible to your workflow logic.

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