How to Use the Ziflow MCP in AutoGen

Q: Can I use Ziflow and AutoGen to manage team access debates?

Yes. You can run a deliberation between agents that check listteamusers against the necessary permissions listed by listfolders. The system only approves access when both perspectives agree.

Q: If I need to audit webhooks with Ziflow and AutoGen, what steps do I take?

An agent calls listwebhooks to gather the list. A second agent then checks against required account parameters using getaccountinfo. The agents debate if the current state is acceptable.

Q: If I need to verify contact information with Ziflow and AutoGen, what data type is stored?

The system touches contact profile data. An agent uses getcontactbyemail to pull the details, and a second agent reviews that data against existing team membership lists for validation.

Drive consensus decisions on content reviews using AutoGen.

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Setup Ziflow with AutoGen

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Perplexity

Debate proof status and final decisions.

One agent uses `get_proof` to pull the current state of a document. A second 'review' agent then critiques that data, challenging assumptions about the content. The process culminates when the agents debate who should make the decision, forcing consensus before finally calling `submit_decision`.

Audit team permissions and access.

An 'auditor' agent starts by listing all available users via `list_team_users`. A second 'security' agent then checks the system against this list, potentially cross-referencing folder access using `list_folders`. This negotiation pattern ensures that a decision on who can view content is based on consensus between security and user data.

Verify workflow setup requirements.

Agent one lists all active webhooks using `list_webhooks`. Agent two then checks the system's core account parameters via `get_account_info`. The agents must agree on both the required webhook status and the current account health before they allow any final action to proceed.

Setup guide

Set up Ziflow 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 Ziflow 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="Ziflow_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Ziflow 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="Ziflow_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Ziflow 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 Ziflow. 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 Ziflow MCP in AutoGen

You set up a multi-agent conversation where agents debate the proof status. They read data from `get_proof` and then, upon consensus, an agent executes `submit_decision`. This is ideal when the final choice isn't obvious.

This server touches user roles, proof metadata, and general account configuration details. Agents use these pieces of data to debate rules and governance policies for your content.

Yes. You can run a deliberation between agents that check `list_team_users` against the necessary permissions listed by `list_folders`. The system only approves access when both perspectives agree.

An agent calls `list_webhooks` to gather the list. A second agent then checks against required account parameters using `get_account_info`. The agents debate if the current state is acceptable.

The system touches contact profile data. An agent uses `get_contact_by_email` to pull the details, and a second agent reviews that data against existing team membership lists for validation.

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