How to Use the Doodle MCP in AutoGen

Q: How do AutoGen agents coordinate Doodle polls?

They call tools like listparticipants to gather facts. Then, they debate the poll status until they reach a decision.

Q: Can AutoGen agents resolve Doodle scheduling conflicts?

They can. If listpolls shows a conflict, the agents discuss the options and use addcomment to suggest alternatives to the group.

Debate your meeting schedule by giving your AutoGen agents direct control over Doodle polls.

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Create your Vinkius account to connect Doodle to AutoGen 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 Doodle with AutoGen

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Debate poll options with AutoGen

Let your agents use `create_poll` as part of their negotiation. One agent suggests a time, another checks the `get_poll` data, and they reach a consensus. This removes the back-and-forth email chain. The agents argue for the best time slot until they agree to finalize the meeting.

Manage participants through debate

Use `add_participant` when agents agree on the guest list. If a participant objects, the agents use `get_comments` to discuss the conflict and adjust accordingly. They negotiate the terms of attendance in real-time. Once they agree, the tool call executes the update to the poll.

Finalize polls via agent consensus

Execute `close_poll` only when the agents confirm the decision. They review the participant list and the voting data to ensure the meeting is set correctly. This ensures you don't close a poll prematurely. The agents verify the data before finalizing, reducing the risk of a bad meeting time.

Setup guide

Set up Doodle 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 Doodle 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="Doodle_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Doodle data")
    print(result.messages[-1].content)

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Common questions about Doodle MCP in AutoGen

They call tools like `list_participants` to gather facts. Then, they debate the poll status until they reach a decision.

They can. If `list_polls` shows a conflict, the agents discuss the options and use `add_comment` to suggest alternatives to the group.

It is. The agents handle the tedious parts of tracking votes and comments, leaving you to review the final meeting confirmation.

You should audit the agent's activity. Watch the logs to see why an agent chose `close_poll` instead of waiting for more votes.

All poll interactions happen through a secure, ephemeral session. Your data is restricted to the agents currently authorized to access the server.

Use it with your favorite AI tools

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