How to Use the Ellie Planner MCP in AutoGen

Q: How do I give my AutoGen agents access to Ellie Planner tools?

Use the mcpservertools function from the autogen-ext library, passing in your Vinkius server URL. This returns a list of tools that you can pass directly to your AssistantAgent constructor.

Q: Can I use AutoGen and Ellie Planner to create a 'janitor' bot?

Definitely. You can create an agent that periodically runs listtasks, identifies tasks with no activity for 30 days, and asks for confirmation before calling deletetask. This is a perfect job for a multi-agent setup.

Let AI agents debate, plan, and manage your Ellie Planner projects through multi-agent conversations in AutoGen.

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Setup Ellie Planner with AutoGen

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Let Agents Debate the Next Move

This is more than automation; it's collaboration. A 'Planner' agent can propose a new feature by calling `create_task`. A 'QA' agent can then use `get_task` to review it, challenge the scope, and suggest adding new labels via `update_task`. The agents converse until they reach a consensus. You're not just executing a plan—you're using an AI team to find the best plan in the first place.

Build a Team of Autonomous Agents

Assign roles and watch them work. A 'Manager' agent can `list_tasks` to find what's overdue, then instruct a 'Worker' agent to call `complete_task` on specific items. Another agent can be tasked with `delete_task` to clean up redundant entries. The entire conversation and execution log is visible. You get a transparent record of which agent did what and why, creating a self-managing system for your projects.

Equip Your Agents for Project Management

Getting started is straightforward. The `mcp_server_tools` function instantly equips your `AssistantAgent` with the entire Ellie Planner toolset. The `McpToolAdapter` handles all the schema and protocol details behind the scenes. This lets your agents focus on the conversation and the strategy. The Ellie Planner MCP Server acts as a shared workspace where your AI team can see project status and execute changes.

Setup guide

Set up Ellie Planner 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 Ellie Planner 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="Ellie Planner_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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 Ellie Planner. 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 Ellie Planner MCP in AutoGen

Use the `mcp_server_tools` function from the `autogen-ext` library, passing in your Vinkius server URL. This returns a list of tools that you can pass directly to your `AssistantAgent` constructor.

Yes, that's the core idea. You can have one agent responsible for creating tasks, another for updating them, and a third for reporting. They collaborate by passing task IDs and instructions to each other in conversation.

A user might ask, 'Plan the server migration.' The planner agent calls `create_task` with sub-steps. A reviewer agent then calls `list_tasks` to see the new items and suggests adding a 'High-Priority' label with `update_task`.

Definitely. You can create an agent that periodically runs `list_tasks`, identifies tasks with no activity for 30 days, and asks for confirmation before calling `delete_task`. This is a perfect job for a multi-agent setup.

Agents pass task data—like titles, IDs, dates, and labels—to each other as plain text within their conversational history. This data lives in memory during the session or in whatever logging system you configure for AutoGen. Secure your agent environment accordingly.

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