How to Use the Flow MCP in AutoGen

Q: How do I register Flow MCP tools with an AutoGen agent?

Use mcpservertools to retrieve the tools from your Vinkius endpoint. You then pass this tool list directly to your AutoGen AssistantAgent constructor, which handles the schema conversion automatically.

Q: Can multiple AutoGen agents use Flow tools in a single conversation?

Yes. Your agents share access to tools like listprojects and getproject. They debate the project status in a shared thread, and the designated execution agent calls the tool when needed.

Q: How do I track what my AutoGen agents do via the MCP Server?

Every write action like addtaskcomment leaves a direct trace on your project board. You can also run the agents in a dry-run mode to inspect their proposed tool calls before they execute.

Let your AutoGen agents debate project priorities and update Flow tasks based on team consensus.

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Setup Flow with AutoGen

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Resolve project bottlenecks with AutoGen

The `get_task` tool allows your AutoGen agents to inspect task details during a live discussion. If a task is blocked, a specialized developer agent queries the details and presents them to the manager agent for review. After debating the issue, the agents use `update_task` to change the priority or assign it to a different team. This ensures that project boards update based on reasoned discussion rather than arbitrary rules.

Collaborative task triaging using the Flow MCP Server

The `create_task` tool lets your AutoGen team generate new action items once they reach a consensus. For example, a quality agent debates a bug report with an engineering agent, and once they agree, they create the task. To keep a clear audit trail, the agent immediately calls `add_task_comment` to log the entire debate transcript. This records the exact reasoning behind the new task directly on the Flow board.

Analyze team workloads with AutoGen

The `list_workspace_members` tool feeds your AutoGen agents the list of active team members to balance workloads. The agents compare this list against active projects to identify who is over-allocated. By combining `list_tasks` and `list_workspace_teams` via MCP, the agents debate how to distribute upcoming work. They negotiate assignments and update the project lists without requiring human intervention.

Setup guide

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

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

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

Use `mcp_server_tools` to retrieve the tools from your Vinkius endpoint. You then pass this tool list directly to your AutoGen `AssistantAgent` constructor, which handles the schema conversion automatically.

Yes. Your agents share access to tools like `list_projects` and `get_project`. They debate the project status in a shared thread, and the designated execution agent calls the tool when needed.

Agents negotiate before executing any write actions. If one agent wants to run `update_task` and another disagrees, they debate the task status until they reach a consensus, preventing conflicting API calls.

Every write action like `add_task_comment` leaves a direct trace on your project board. You can also run the agents in a dry-run mode to inspect their proposed tool calls before they execute.

Yes. Vinkius secures your workspace and member data using zero-trust V8 isolates. Your API keys are encrypted at rest, and tool executions run in sandboxed environments that delete all data once the session ends.

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