How to Use the Unstructured MCP in Vercel AI SDK

Q: How do I check available data sources using Unstructured and Vercel AI SDK?

You call listdatasources via your agent. The MCP Server returns the list of configured connectors (like S3 or GCS), which you then stream directly into your Next.js UI, showing the user what feeds are connected.

Q: What data does Unstructured provide about workflow status for Vercel AI SDK?

The listworkflowjobs tool gives you both active and historical job IDs and their current status. This mechanism allows your agent to report the real-time progress of complex document processing directly into the user's browser.

Q: Can I list data destinations for Unstructured using Vercel AI SDK?

Absolutely. Use listdatadestinations to get a comprehensive list of all configured endpoints, including specific Vector DBs and SQL targets. This information streams immediately, letting your frontend build out the necessary connection UI.

Q: What is the mechanism for triggering an unstructured job with Vercel AI SDK?

To kick off a run, you invoke triggerworkflowexecution. The MCP Server returns a unique job ID right away. You then use this ID to track the progress of that specific workflow execution in your streaming UI.

Show live data processing results directly in your Next.js or React frontend using the Vercel AI SDK.

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Setup Unstructured with Vercel AI SDK

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Visualize Data Sources on the Frontend

Need to show users where your data is coming from? Use `list_data_sources` to pull a list of configured remote connectors (S3, GCS) and display that list in real time. Your frontend receives this information as soon as it's processed by the MCP Server. You can even let users trigger a check using `list_data_destinations`. This lets your agent query all target locations—whether they're SQL databases or Vector DBs—and stream the results instantly into the user interface.

Monitor Workflow Jobs in Real-Time

Building a dashboard that tracks background tasks? The `list_workflow_jobs` tool lets your agent fetch all historical and active job records. You can stream these status updates to the user, letting them see if their document pipeline is running or if it failed. This immediate feedback loop means users never stare at a spinner; they see the progress of the unstructured data transformation happening live as results appear.

Check Processing Pipeline Configurations

Before letting a user run anything, you'll want them to review the setup. Use `list_processing_workflows` to fetch all defined end-to-end pipelines. You can present this list directly in your React component for easy client inspection. If they need the specific rules for one pipeline, call `get_workflow_details`. This gives you the config details needed to display complex processing logic right where the user is interacting with it.

Setup guide

Set up Unstructured MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Unstructured tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Unstructured transactions",
});

console.log(text);
await mcpClient.close();

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Unstructured. 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 Unstructured MCP in Vercel AI SDK

You call `list_data_sources` via your agent. The MCP Server returns the list of configured connectors (like S3 or GCS), which you then stream directly into your Next.js UI, showing the user what feeds are connected.

The `list_workflow_jobs` tool gives you both active and historical job IDs and their current status. This mechanism allows your agent to report the real-time progress of complex document processing directly into the user's browser.

Absolutely. Use `list_data_destinations` to get a comprehensive list of all configured endpoints, including specific Vector DBs and SQL targets. This information streams immediately, letting your frontend build out the necessary connection UI.

To kick off a run, you invoke `trigger_workflow_execution`. The MCP Server returns a unique job ID right away. You then use this ID to track the progress of that specific workflow execution in your streaming UI.

This server deals with transforming complex unstructured data into clean, AI-ready inputs. You'll primarily be dealing with connection metadata (sources and destinations) and job status reports.

Use it with your favorite AI tools

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