How to Use the Unstructured MCP in CrewAI

Q: How do I check available data sources for unstructured using CrewAI?

Use listdatasources. This tool provides a shared, definitive list of all configured remote connectors (S3, GCS). The agents use this information to scope their research and ensure data access permissions are correct.

Q: What job status does Unstructured provide for CrewAI?

The listworkflowjobs tool provides the shared memory of all active and historical workflow execution jobs. This allows the monitor agent to track progress and report state changes across the entire autonomous team.

Q: Can I check data destinations for unstructured using CrewAI?

Yes, listdatadestinations lets your crew confirm all target locations. This is essential because agents need to know if they're writing results to a Vector DB or an SQL database before the final action.

Q: What mechanism do I use to start unstructured work with CrewAI?

To initiate a team effort, you call triggerworkflowexecution. This returns the job ID which becomes part of the shared memory. The crew then monitors this job's progress until completion.

Build autonomous teams that manage complex unstructured data operations with CrewAI.

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Create your Vinkius account to connect Unstructured to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Unstructured with CrewAI

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Orchestrate Data Collection Tasks

If Agent A needs to gather background info, have it use `list_data_sources` to check all available remote connectors. This ensures the agent knows exactly which S3 or GCS buckets are accessible for research. Later, if a team needs to act on that data, they can verify where the results will go using `list_data_destinations`, confirming the correct Vector DB or SQL endpoint.

Manage and Trigger Multi-Agent Workflows

The core function is initiating work. Use `trigger_workflow_execution` to kick off a complex job run, returning a vital job ID for shared memory across agents. This lets the monitor agent watch the session while other agents collaborate on the outcome. Agents can also review the entire process definition using `list_processing_workflows`, ensuring everyone knows what the end-to-end goal is.

Audit and Debug Agent Operations

When a team needs to troubleshoot, they need visibility. The `list_workflow_jobs` tool gives every agent access to all historical and active job records. This shared memory prevents agents from repeating work or acting on outdated status reports. If the process fails, `get_workflow_details` lets the moderator agent pull the exact configuration rules that were supposed to be followed.

Setup guide

Set up Unstructured MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Unstructured tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Unstructured Analyst",
    goal="Access and analyze Unstructured data via MCP.",
    backstory="Expert analyst with direct Unstructured access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Unstructured transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Unstructured Analyst",
        goal="Access and analyze Unstructured data via MCP.",
        backstory="Expert analyst with direct Unstructured access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Unstructured transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 CrewAI

Use `list_data_sources`. This tool provides a shared, definitive list of all configured remote connectors (S3, GCS). The agents use this information to scope their research and ensure data access permissions are correct.

The `list_workflow_jobs` tool provides the shared memory of all active and historical workflow execution jobs. This allows the monitor agent to track progress and report state changes across the entire autonomous team.

Yes, `list_data_destinations` lets your crew confirm all target locations. This is essential because agents need to know if they're writing results to a Vector DB or an SQL database before the final action.

To initiate a team effort, you call `trigger_workflow_execution`. This returns the job ID which becomes part of the shared memory. The crew then monitors this job's progress until completion.

The server handles connection metadata (sources and destinations), end-to-end processing pipelines, and job execution reports. This allows your multi-agent team to manage complex information flow autonomously.

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