How to Use the Unstructured MCP in Mastra AI

Q: How do I view data sources when configuring an unstructured flow in Mastra AI?

Call listdatasources. This gives your agent a full list of all configured remote connectors (S3, GCS). Your workflow engine uses this list to ensure the starting point for the job is valid before proceeding.

Q: What data does Unstructured provide about job status in Mastra AI?

The listworkflowjobs tool provides active and historical execution records. This allows your multi-step workflow to check the state of a process, triggering retries or notifications based on the returned status.

Q: Can I list data destinations for unstructured when building workflows in Mastra AI?

Yes, you use listdatadestinations. This shows all possible targets—Vector DBs and SQL endpoints. Your framework uses this to determine the correct fallback or successful writing location after a job completes.

Q: How do I initiate an unstructured workflow run using Mastra AI?

You use triggerworkflowexecution. This tool immediately initiates the process and returns a unique job ID. Your agent uses this ID to monitor the progress, which is critical for multi-step failure handling.

Build reliable, multi-step workflows that handle failures using the Mastra AI agent framework.

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Manage Complex Data Pipelines

When you need to process data through multiple stages, start by listing all available pipelines with `list_processing_workflows`. This gives your built-in workflow engine the context it needs. You can then use `get_workflow_details` to check the specific configuration rules for any single pipeline before running it. This control lets you build robust multi-step workflows that won't fail silently when dealing with messy unstructured data.

Monitor Job Execution and History

Handling failures requires visibility. Use `list_workflow_jobs` to pull a full audit log of every job run—both successful ones and the failed attempts. The Mastra AI client uses this history for its built-in retry logic. It's crucial for debugging; you can see exactly when and why a workflow stalled, which is essential for reliable, automated operations.

Identify Data Input/Output Points

Before any job runs, the system needs to know its inputs and outputs. Check all available data feeds using `list_data_sources` or confirm target storage with `list_data_destinations`. These tools provide the necessary metadata for conditional branching within your workflow. Knowing these points guarantees that if a step fails (like writing to SQL), you can execute the defined retry logic against alternate targets.

Setup guide

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

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

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

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Common questions about Unstructured MCP in Mastra AI

Call `list_data_sources`. This gives your agent a full list of all configured remote connectors (S3, GCS). Your workflow engine uses this list to ensure the starting point for the job is valid before proceeding.

The `list_workflow_jobs` tool provides active and historical execution records. This allows your multi-step workflow to check the state of a process, triggering retries or notifications based on the returned status.

Yes, you use `list_data_destinations`. This shows all possible targets—Vector DBs and SQL endpoints. Your framework uses this to determine the correct fallback or successful writing location after a job completes.

You use `trigger_workflow_execution`. This tool immediately initiates the process and returns a unique job ID. Your agent uses this ID to monitor the progress, which is critical for multi-step failure handling.

This MCP Server handles workflow metadata: connection details (sources and destinations), processing pipeline configurations, and execution job reports. It's all about managing the flow of structured information.

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