How to Use the Unstructured MCP in AutoGen

Q: How do I find out what kind of data sources Unstructured supports when using AutoGen?

The agents use listdatasources to retrieve a list of all configured remote connectors. This information is passed into the conversation, allowing one agent to fact-check another's proposed architecture.

Q: If my workflow fails, how can I check its history with Unstructured and AutoGen?

The listworkflowjobs tool gives a full audit trail of job executions. This allows the debating agents to review failure points and refine their strategy based on concrete historical evidence.

Q: What kind of unstructured metadata can I query using Unstructured and AutoGen?

The MCP Server exposes configuration details via getworkflowdetails. The agents debate the parameters, ensuring they understand every dependency before proceeding with a final decision.

Q: Can I list all possible processing pipelines with Unstructured and AutoGen?

Yes. listprocessingworkflows gives a comprehensive catalogue of available pipelines. This allows the multi-agent system to understand its full operational scope during deliberation.

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Query Sources and Destinations via MCP Server

When agents argue over data access, one agent can verify the available endpoints. The `list_data_sources` tool confirms all configured remote connectors (S3, GCS). This prevents agents from making assumptions about connectivity. Another agent uses `list_data_destinations` to check where results *must* go—be it a Vector DB or SQL. The debate converges on a plan that respects these constraints.

Review Workflow Status with MCP Server

A group of agents debating a data strategy needs clear oversight. One agent runs `list_processing_workflows` to get the full list of available pipelines. This keeps the conversation grounded in reality. Later, if they need confirmation on execution feasibility, they use `get_workflow_details`. The MCP Server provides the precise configuration needed for the agents to debate a final, actionable plan.

Trigger and List Job Runs with AutoGen

If the consensus requires an immediate action, the system can call `trigger_workflow_execution`, getting a job ID. This initiates the process that all agents must wait for. A different agent uses `list_workflow_jobs` to monitor the execution status of that triggered job or review past performance. The debate ends when this history confirms completion.

Setup guide

Set up Unstructured MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Unstructured tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Unstructured_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Unstructured data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Unstructured_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Unstructured data")
    print(result.messages[-1].content)

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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 AutoGen

The agents use `list_data_sources` to retrieve a list of all configured remote connectors. This information is passed into the conversation, allowing one agent to fact-check another's proposed architecture.

The `list_workflow_jobs` tool gives a full audit trail of job executions. This allows the debating agents to review failure points and refine their strategy based on concrete historical evidence.

The MCP Server exposes configuration details via `get_workflow_details`. The agents debate the parameters, ensuring they understand every dependency before proceeding with a final decision.

Yes. `list_processing_workflows` gives a comprehensive catalogue of available pipelines. This allows the multi-agent system to understand its full operational scope during deliberation.

This server handles metadata and configuration definitions for unstructured data sources, including connector endpoints and workflow settings. This metadata is the focus of the agents' debate.

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