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.

Unstructured MCP Server for AutoGen 6 tools — connect in under 2 minutes

Name: Unstructured
Availability: InStock
Author: Vinkius

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 6 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Unstructured as an MCP tool provider through the Vinkius and every agent in the group can access live data and take action.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="unstructured_agent",
            tools=tools,
            system_message=(
                "You help users with Unstructured. "
                "6 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Unstructured MCP Server

Connect your Unstructured.io account to any AI agent to automate data ingestion and document processing pipelines seamlessly. Transform complex files into clean, AI-ready data without leaving your workflow.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Unstructured tools. Connect 6 tools through the Vinkius and assign role-based access — a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

What you can do

Data Sources — List all configured remote data connectors (e.g. S3, GCS, SharePoint) to see where documents can be pulled from.
Data Destinations — Browse target locations (like Vector DBs or SQL databases) where structured output is sent.
Processing Workflows — List end-to-end pipelines, retrieve specific workflow configurations, and explore source-destination mappings.
Job Execution — Manually trigger immediate document ingestion and partitioning jobs, and track their execution IDs.
Job Monitoring — List active and historical workflow execution jobs to monitor the progress of your document processing tasks.

The Unstructured MCP Server exposes 6 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Unstructured to AutoGen via MCP

Follow these steps to integrate the Unstructured MCP Server with AutoGen.

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 6 tools from Unstructured automatically

Why Use AutoGen with the Unstructured MCP Server

AutoGen provides unique advantages when paired with Unstructured through the Model Context Protocol.

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Unstructured tools to solve complex tasks

Role-based architecture lets you assign Unstructured tool access to specific agents — a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive Unstructured tool calls

Code execution sandbox: AutoGen agents can write and run code that processes Unstructured tool responses in an isolated environment

Unstructured + AutoGen Use Cases

Practical scenarios where AutoGen combined with the Unstructured MCP Server delivers measurable value.

Collaborative analysis: one agent queries Unstructured while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from Unstructured, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using Unstructured data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process Unstructured responses in a sandboxed execution environment

Unstructured MCP Tools for AutoGen (6)

These 6 tools become available when you connect Unstructured to AutoGen via MCP:

get_workflow_details

Retrieves configuration details for a specific processing workflow

list_data_destinations

g. Vector DBs, SQL). Lists all configured target locations for processed data

list_data_sources

Lists all configured remote data connectors (e.g. S3, GCS)

list_processing_workflows

Lists all end-to-end document processing pipelines

list_workflow_jobs

Lists all active and historical workflow execution jobs

trigger_workflow_execution

Returns a job ID. Manually triggers an immediate execution of a processing workflow

Example Prompts for Unstructured in AutoGen

Ready-to-use prompts you can give your AutoGen agent to start working with Unstructured immediately.

"Show me all our active destination connectors."

"List the historical processing jobs from today."

"Trigger the engineering onboarding workflow."

Troubleshooting Unstructured MCP Server with AutoGen

Common issues when connecting Unstructured to AutoGen through the Vinkius, and how to resolve them.

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

Unstructured + AutoGen FAQ

Common questions about integrating Unstructured MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Unstructured tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.