Unstructured Connector for AI agents.
6 live capabilities
Manage document ingestion pipelines and RAG data workflows.
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Why people use Unstructured
Unstructured for RAG Data Ingestion
With this Connector, that manual loop disappears. You stay in your editor and ask your agent to trigger a job, check the status, or list your sources. You get a single command center for your data flow, which means you spend less time babysitting pipelines and more time building features.
What Vinkius changes
You get a command center for your RAG data pipelines inside your existing AI workflow.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Automated RAG updates
A developer wants to sync SharePoint docs to a Vector DB.
- Real-world use case 02
Onboarding document processing
An HR tech lead needs to process 500 engineering PDFs.
- Real-world use case 03
Source auditing
A data engineer needs to see all active S3 connections.
Complete set · 6capabilities
The complete Unstructured capability set.
These are the exact actions your AI can choose when you ask it to work with Unstructured.
01—03
3 capabilities in this set.
Part of 6 available through Unstructured.
- 01 Capability
Get workflow details
Retrieves configuration details for a specific processing workflow. Use it to check exact source and destination rules.
- 02 Capability
Trigger workflow execution
Manually triggers an immediate execution of a processing workflow. It returns a job ID so you can track the progress.
- 03 Capability
List data destinations
Lists all configured target locations for processed data. Use this to confirm your Vector DB or SQL links are active.
04—06
3 capabilities in this set.
Part of 6 available through Unstructured.
- 04 Capability
List processing workflows
Lists all end-to-end document processing pipelines. This helps you see every ingestion path you have set up.
- 05 Capability
List workflow jobs
Lists all active and historical workflow execution jobs. Use this to quickly see if your latest ingestion finished.
- 06 Capability
List data sources
Lists all configured remote data connectors. This lets you see all active S3, GCS, or SharePoint connections.
Set up in minutes
One URL. Then ask Unstructured to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Unstructured from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_wLKl0zBun6GGEWOAXV2AtIEfq4BCwiQMTDSCBvt6/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Unstructured, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Unstructured for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_wLKl0zBun6GGEWOAXV2AtIEfq4BCwiQMTDSCBvt6/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Unstructured URL.
- Step 03
Save and start
Save the connection and enable Unstructured in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"unstructured": {
"url": "https://edge.vinkius.com/vk_preview_wLKl0zBun6GGEWOAXV2AtIEfq4BCwiQMTDSCBvt6/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Unstructured
Open Agent mode in chat and ask: "Using Unstructured, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"unstructured": {
"url": "https://edge.vinkius.com/vk_preview_wLKl0zBun6GGEWOAXV2AtIEfq4BCwiQMTDSCBvt6/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Unstructured
Ask Copilot: "Using Unstructured, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"unstructured": {
"url": "https://edge.vinkius.com/vk_preview_wLKl0zBun6GGEWOAXV2AtIEfq4BCwiQMTDSCBvt6/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Unstructured
Open Cascade and ask: "Using Unstructured, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"unstructured": {
"url": "https://edge.vinkius.com/vk_preview_wLKl0zBun6GGEWOAXV2AtIEfq4BCwiQMTDSCBvt6/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Unstructured
Ask Cline: "Using Unstructured, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add unstructured --transport http "https://edge.vinkius.com/vk_preview_wLKl0zBun6GGEWOAXV2AtIEfq4BCwiQMTDSCBvt6/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Unstructured
Ask Claude: "Using Unstructured, show me...". 6 tools are ready
Where the request belongs
Work Unstructured can move forward.
This is for the data engineer tired of manual ETL work, the AI dev building RAG systems, and the MLOps pro who needs to monitor document syncs without constant dashboard refreshes.
Data Engineer
Troubleshoots and triggers ingestion workflows for messy PDFs and docs without leaving the terminal.
AI Developer
Monitors RAG pipelines to ensure vector databases are populating with clean, partitioned data.
MLOps Engineer
Tracks historical processing jobs and verifies that scheduled syncs from SharePoint or S3 completed successfully.
When one Connector is not enough
Carry the request into a workflow.
Combine Unstructured with the systems that finish the task.
View all recipesBuild the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsLlamaCloud (Managed RAG & Parsing)
Manage RAG pipelines and document parsing via LlamaCloud. orchestrate LlamaParse jobs and audit data ingestion.
Extracta
Automate data extraction via Extracta. process documents into structured JSON, handle AI classification, and audit extraction history directly from any AI agent.
DocSumo
Equip your AI agent to automate document data extraction, manage IDP workflows, and audit processed files via the DocSumo API.
LlamaIndex (AI Data Framework & RAG)
Query and manage RAG pipelines via LlamaIndex. execute natural language searches, audit indexed files, and monitor data pipelines.
Docparser
Equip your AI agent to extract data from documents, manage parsers, and track extraction results via the Docparser API.
PipeStream
Streamline real-time data flows. create streams, publish events, and fetch historical data directly from your AI agent.
Bring your own AI
Change the model, client or framework. Keep Unstructured connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Unstructured.
The practical details behind the request, access and result.
Can I use the Unstructured MCP to manage my RAG data pipelines?
Yes. It gives your AI agent the ability to trigger jobs, check statuses, and manage sources for your RAG system directly from your workspace.
Does the Unstructured MCP support S3 and SharePoint?
Yes, it allows your agent to interact with various remote data connectors including S3, GCS, and SharePoint to pull in documents for processing.
How do I check if my document processing job finished?
You can just ask your agent to list the workflow jobs. It will show you the status of both active and completed tasks with their specific IDs.
Can I trigger a new ingestion job using this Connector?
Absolutely. You can tell your agent to start a specific workflow immediately, and it will trigger the job and provide you with a tracking ID.
Is this Connector good for handling large PDF files?
Yes, it's designed specifically to manage the ingestion and partitioning of complex files like PDFs, Word docs, and HTML into clean data.
What happens if a data job fails using the Unstructured MCP?
Your agent can retrieve the specific configuration details for that workflow, helping you identify whether the issue is with the source, the destination, or the processing rules.
Can my AI agent trigger an immediate document processing job?
Yes! If you have a workflow configured to pull files from an S3 bucket and load them into a Pinecone index, you can ask your agent to trigger workflow XYZ. It will start the execution and return the new Job ID, which you can use to track the progress.
How can I verify if my RAG pipelines are failing or succeeding?
Ask your agent to list your workflow jobs. It will securely connect to Unstructured's engine and return historical and active executions, displaying statuses such as 'completed', 'failed', or 'in_progress'. This is extremely useful for MLOps engineers diagnosing ingestion alerts directly in their terminal.
Can I edit the destination database directly through the agent?
This server is focused on auditing and executing your existing pipelines. Currently, you can list all connections (sources and destinations) and obtain their details, but creating or destructively modifying vector database connectors must be done inside the Unstructured dashboard for security.
One connection away
Give your agent a direct line to Unstructured.
Connect Unstructured once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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