How to Use the Unstructured MCP in Cursor

Q: How do I get Unstructured configured in Cursor?

You add the server configuration by creating or updating a .cursor/mcp.json file in your project root or global config location.

Q: Can I use Unstructured to debug a failing workflow job in Cursor?

Yes. You can call listworkflowjobs to retrieve the run ID and status, giving your AI client the context it needs to write fixes or proper logging code.

Q: What happens if I need to list available data connectors?

You use the listdatasources tool. This provides a full manifest of all configured remote connections, which your AI client can then reference when writing integration code.

Write code using live data from Unstructured with Cursor for context-aware results.

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Connect Unstructured MCP to Cursor

Create your Vinkius account to connect Unstructured to Cursor 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 Unstructured with Cursor

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Get real API responses into your code

The `list_data_sources` tool shows all remote connectors, allowing the AI to inject variables like bucket names or connection strings directly into the function you're writing. You don't get mock data—you get context. The same works when generating setup code using `list_data_destinations`, ensuring your database credentials are accurate before compile time.

Manage processing pipelines with Unstructured MCP Server

Need to know what's happening downstream? Use `list_processing_workflows` to list all defined document pipelines. You can then use `get_workflow_details` in your code comments to reference the exact parameters needed for a specific workflow run. This makes writing integrated, data-aware functions much easier.

Trigger and track jobs right from Cursor

Want to test a pipeline instantly? Calling `trigger_workflow_execution` gives you the job ID, which you can then write into your code's state management. To check history, use `list_workflow_jobs`; it returns all execution records needed for debugging. It lets you bake live data checks right into your coding flow.

Setup guide

Set up Unstructured MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Unstructured MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Unstructured transactions." If the MCP tools are loaded correctly, Cursor will call the Unstructured tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "unstructured-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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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 Cursor

You add the server configuration by creating or updating a `.cursor/mcp.json` file in your project root or global config location.

It provides metadata about sources (like S3 connectors) and destinations (like SQL endpoints), letting the AI client write functional code that interacts with those specific types of services.

Yes. You can call `list_workflow_jobs` to retrieve the run ID and status, giving your AI client the context it needs to write fixes or proper logging code.

You use the `list_data_sources` tool. This provides a full manifest of all configured remote connections, which your AI client can then reference when writing integration code.

The server manages workflows that transform complex data into AI-ready inputs by managing sources and destinations, making it ideal for varied document processing needs.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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