How to Use the Flow XO MCP in AutoGen

Q: What happens if an AutoGen agent fails to run a Flow XO tool?

If a tool call like updateuser fails, the error is returned to the group chat. A recovery agent analyzes the error message, adjusts the payload parameters, and attempts the tool call again.

Run multi-agent debates to manage Flow XO workflows and chatbot users with AutoGen.

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Setup Flow XO with AutoGen

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Multi-Agent Consensus on Flow XO Workflows

The `toggle_workflow` tool is managed through multi-agent negotiation to prevent accidental downtime. In this setup, a performance agent might suggest disabling a flow, while a compliance agent reviews the configuration before executing the change. The agents debate the operational impact using data retrieved from `list_workflows`. Once they reach a consensus, the designated execution agent calls the tool to update the workflow status.

Collaborative Push Campaigns in AutoGen

The `send_push_message` tool is executed only after your AutoGen agents agree on the optimal message content. A copywriter agent drafts the push notification, while an analyst agent checks user history using `list_user_history` to ensure it fits the context. This collaborative loop reduces spam and ensures high-quality outreach. Once both agents approve, the message is dispatched to the target recipient identified by `list_chatbot_users`.

Automated Webhook Triage

The `trigger_webhook` tool allows your agent group to initiate external automated sequences when specific system events occur using this MCP Server. A coordinator agent monitors system status and coordinates with specialized sub-agents to decide if a trigger is necessary. Before firing, a validation agent calls `get_automation_analytics` to confirm the system is operating within safe performance thresholds. This multi-agent verification prevents runaway webhook loops and API rate-limiting issues.

Setup guide

Set up Flow XO 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 Flow XO 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="Flow XO_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Your agents communicate via a group chat manager, passing tool suggestions like `toggle_workflow` as proposals. A referee agent validates the proposal against current logs from `list_user_history` before allowing the execution agent to make the call.

Yes, you configure the server using StreamableHttpServerParams with your Vinkius connection URL. This allows your AutoGen assistant agents to access tools like `trigger_webhook` and `create_user` over secure web sockets or HTTP.

You fetch the tools using the MCP client adapter, which automatically translates the schemas. Then, you pass the resulting tools list directly into the AssistantAgent's constructor so they are ready for the conversation loop.

If a tool call like `update_user` fails, the error is returned to the group chat. A recovery agent analyzes the error message, adjusts the payload parameters, and attempts the tool call again.

Your bot account credentials and configurations, accessed via `list_bot_accounts`, are transmitted over encrypted TLS directly to your local AutoGen environment. Vinkius hosts the MCP Server in an isolated, secure container, ensuring your platform credentials are never exposed to external LLM providers.

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