How to Use the ProcessOn MCP in CrewAI

Q: How do I configure ProcessOn tools for a CrewAI agent?

Pass the MCP Server URL directly into the agent's mcps array parameter during initialization. For more granular control, use MCPServerHTTP to filter which tools like createfile or exportfile are exposed to specific agents.

Q: Can a CrewAI team manage file sharing in ProcessOn?

Yes, your agents can use listcollaborators to inspect sharing settings. They can coordinate to ensure the right team members have access to newly created mind maps and flowcharts.

Run a team of AI agents to build, organize, and export ProcessOn diagrams autonomously with CrewAI.

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Create your Vinkius account to connect ProcessOn to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup ProcessOn with CrewAI

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Deploy specialized agents with this MCP Server

Instead of one generic assistant, this MCP Server lets CrewAI run a team of specialists. You can have a Researcher agent map out ideas, while an Organizer agent uses `create_file` and `get_folder_content` to structure the workspace. These agents share memory and pass context down the line. The final output is a clean, organized set of diagrams that match your project requirements without manual intervention.

Automate collaborative workspace audits

Let your crew monitor who is working on what. A dedicated Auditor agent can call `list_collaborators` and `get_recent_files` to compile a report on active projects. If the agent detects unauthorized access, it can flag the file or notify your admin. This continuous monitoring keeps your organizational assets secure without wasting developer hours.

Export and distribute team assets autonomously

Once your design agents finish updating a flowchart, the pipeline doesn't stop. An export specialist agent invokes `export_file` and `get_file` to fetch the finished assets. The crew then delivers these files to your downstream channels. Because execution can be sequential or hierarchical, you control the exact flow of data from raw idea to exported image.

Setup guide

Set up ProcessOn 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 ProcessOn tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent ProcessOn 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="ProcessOn Analyst",
        goal="Access and analyze ProcessOn data via MCP.",
        backstory="Expert analyst with direct ProcessOn access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent ProcessOn 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 ProcessOn. 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 ProcessOn MCP in CrewAI

Pass the MCP Server URL directly into the agent's `mcps` array parameter during initialization. For more granular control, use `MCPServerHTTP` to filter which tools like `create_file` or `export_file` are exposed to specific agents.

Yes, your agents can use `list_collaborators` to inspect sharing settings. They can coordinate to ensure the right team members have access to newly created mind maps and flowcharts.

CrewAI agents use shared memory to pass file IDs and folder paths between tasks. One agent can call `create_file` and pass the resulting ID to another agent that handles the export step.

The crew manager agent logs the failure and can re-assign the task or attempt a retry depending on your execution strategy. This prevents a single failed API call from breaking your entire pipeline.

Yes, all mind map node content and organization info are routed securely through transient HTTPS requests. The zero-trust MCP environment ensures that no credentials or sensitive diagram data are ever written to persistent storage.

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