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 OpenAI Agents SDK 6 tools — connect in under 2 minutes

Name: Unstructured
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 6 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Unstructured through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

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 agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Unstructured Assistant",
            instructions=(
                "You help users interact with Unstructured. "
                "You have access to 6 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Unstructured"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

The OpenAI Agents SDK auto-discovers all 6 tools from Unstructured through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Unstructured, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP

Follow these steps to integrate the Unstructured MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 6 tools from Unstructured

Why Use OpenAI Agents SDK with the Unstructured MCP Server

OpenAI Agents SDK provides unique advantages when paired with Unstructured through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Unstructured + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Unstructured MCP Server delivers measurable value.

Automated workflows: build agents that query Unstructured, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Unstructured, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Unstructured tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Unstructured to resolve tickets, look up records, and update statuses without human intervention

Unstructured MCP Tools for OpenAI Agents SDK (6)

These 6 tools become available when you connect Unstructured to OpenAI Agents SDK 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 OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK

Common issues when connecting Unstructured to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Unstructured + OpenAI Agents SDK FAQ

Common questions about integrating Unstructured MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.