How to Use the Dataiku DSS MCP in Mastra AI

Q: What happens if a Dataiku DSS job fails in Mastra AI?

The agent detects the failure through getjob metadata. You can script a retry policy that kicks in automatically if the error is temporary.

Build complex, failure-resilient Dataiku DSS workflows using the Mastra AI framework for automated data science operations.

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Connect Dataiku DSS MCP to Mastra AI

Create your Vinkius account to connect Dataiku DSS 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 Dataiku DSS with Mastra AI

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Automate Dataiku DSS recipes with Mastra AI

Chain your data transformations by invoking `list_recipes` and `run_scenario` within your agent workflows. You can build logic that checks recipe configurations before triggering a build. If a job fails, your agent catches the state via `get_job`. It then decides whether to retry or alert your team based on the rules you set.

Manage connections in Dataiku DSS using Mastra AI

Audit your infrastructure by calling `list_connections` to see exactly where your data lives. Your agents can verify that all required databases are active before starting a pipeline. This keeps your operations transparent. You'll know exactly which cloud storage or API is currently linked to your project.

Coordinate multi-step Dataiku DSS tasks in Mastra AI

Use `list_projects` to scope your agent's actions to specific environments. You define the sequence, and the agent iterates through the tasks until the job finishes. It removes the manual oversight for routine maintenance. Your agent tracks the progress and reports back once the scenario execution is complete.

Setup guide

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

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

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

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Common questions about Dataiku DSS MCP in Mastra AI

You register the server in your Mastra configuration. This makes the tools available for your agent to use in any step of your workflow.

It can. You can set the agent to require your human sign-off before it runs any scenario that changes your production pipeline.

The agent detects the failure through `get_job` metadata. You can script a retry policy that kicks in automatically if the error is temporary.

Yes, you use the tool results to drive conditional logic. If the model metadata from `get_model` shows low accuracy, the agent takes a different path.

We isolate your interaction in a Vinkius sandbox. Your project metadata and job logs are encrypted in transit and never stored on our side.

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