How to Use the Unstructured MCP in Claude Code

Q: How do I process unstructured data using Unstructured with Claude Code?

Run a script that first checks available sources using listdatasources, then uses triggerworkflowexecution to start the job, and finally polls the status via listworkflowjobs. It's all done in the terminal.

Q: Can I list jobs when using Unstructured with Claude Code?

Yes. The listworkflowjobs function lets you check both active and historical job execution records directly from your shell script or cron job.

Q: Does the MCP Server support checking source availability in Claude Code?

Yep. You call listdatasources to list every configured remote data connector, giving your CI/CD pipeline full visibility into where it can grab input files.

Run Unstructured data processing pipelines headlessly from the terminal with Claude Code.

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Setup Unstructured with Claude Code

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Perplexity

Manage workflows for CI/CD scripts.

You can script complex operations by calling `trigger_workflow_execution` and piping the resulting job ID to another tool. This is ideal for automated testing or nightly runs that need immediate processing. Before running, use `list_processing_workflows` to ensure your shell script targets the correct end-to-end document pipeline.

List all remote data connectors from the terminal.

Need a script that pulls documents from an external location? Use `list_data_sources` to print out every configured remote connector (S3, GCS). This output can then be used by subsequent tools in your pipeline. It also prints all possible destinations using `list_data_destinations`, which you'll need for the final write step.

Get detailed workflow configuration via MCP Server.

To validate a job, run `get_workflow_details` to pull specific config parameters. You can also list all historical jobs and active ones using `list_workflow_jobs`, which is key for auditing in a headless environment. This allows you to build robust scripts that check the status of data movement before declaring success.

Setup guide

Set up Unstructured MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see unstructured-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest Unstructured transactions." It will automatically discover and invoke the available Unstructured tools.

Terminal

claude mcp add --transport http unstructured-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Get your connection token →

Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the Unstructured MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

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

Get your connection token →

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Common questions about Unstructured MCP in Claude Code

Run a script that first checks available sources using `list_data_sources`, then uses `trigger_workflow_execution` to start the job, and finally polls the status via `list_workflow_jobs`. It's all done in the terminal.

The server manages source credentials (S3/GCS) and processes raw documents into structured records destined for Vector DBs or SQL databases, all visible in the terminal output.

Yes. The `list_workflow_jobs` function lets you check both active and historical job execution records directly from your shell script or cron job.

Yep. You call `list_data_sources` to list every configured remote data connector, giving your CI/CD pipeline full visibility into where it can grab input files.

The server requires access credentials for external sources and destinations. When running in a headless environment, ensure your service account has the minimum required permissions to run these tools safely.

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