How to Use the Circadian REM Sleep Cycle Optimizer MCP in AutoGen

Q: Can one agent call calculatesleepdebt and another agent react to that data in AutoGen?

Yes, that's the core workflow. One agent executes the calculatesleepdebt tool, and its output is posted in the agent chat. Other agents see this new information and can trigger their own tools or change their recommendations based on it.

Let your AutoGen agents debate the best sleep schedule for peak performance.

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Connect Circadian REM Sleep Cycle Optimizer MCP to AutoGen

Create your Vinkius account to connect Circadian REM Sleep Cycle Optimizer 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 Circadian REM Sleep Cycle Optimizer with AutoGen

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Debate Your Daily Schedule

Set up a team of agents with different goals. A 'Performance Agent' might want you to work late to finish a project. It will argue for pushing through the evening. A 'Wellness Agent' can counter that argument with data. It calls `calculate_sleep_debt` and presents the facts: 'Working late will add two hours to your sleep debt.' Then it runs `calculate_sleep_cycles` to show how that damages your REM sleep. The agents debate and converge on a smarter plan.

Consensus-Driven Nap Planning

Imagine you hit an afternoon slump. A 'Task Manager' agent might just tell you to drink coffee and keep going. It sees only the goal: complete the task. But a 'Bio-Hacker' agent can call `calculate_circadian_rhythm` and see you're in a natural energy trough. It then uses `calculate_nap_times` to propose a specific, 25-minute power nap. The agents discuss the data and agree that the nap is the better strategy for overall productivity.

Assemble a Sleep Council in AutoGen

The `mcp_server_tools` function gets you the list of available sleep tools from this MCP server. You can then create specialized agents by giving them different tools. For instance, give the `calculate_sleep_debt` tool to an 'Accountant' agent and `calculate_circadian_rhythm` to a 'Planner' agent. This forces them to collaborate. The Planner can't create a good schedule without getting the current sleep debt from the Accountant. This multi-agent conversation is exactly what AutoGen was designed for, and these tools provide the perfect subject matter for their debate.

Setup guide

Set up Circadian REM Sleep Cycle Optimizer 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 Circadian REM Sleep Cycle Optimizer 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="Circadian REM Sleep Cycle Optimizer_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Circadian REM Sleep Cycle Optimizer 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="Circadian REM Sleep Cycle Optimizer_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Circadian REM Sleep Cycle Optimizer data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Circadian REM Sleep Cycle Optimizer MCP in AutoGen

You can assign different tools to different agents. For example, one agent can be the expert on `calculate_sleep_debt`, while another uses `calculate_sleep_cycles`. They then converse, sharing their findings to reach a group decision.

Try a 'Performance' agent focused on goals and a 'Wellness' agent armed with all the sleep tools. When you give them a task, they will debate the best way to achieve it without accumulating sleep debt, using the tool outputs as evidence.

Yes, that's the core workflow. One agent executes the `calculate_sleep_debt` tool, and its output is posted in the agent chat. Other agents see this new information and can trigger their own tools or change their recommendations based on it.

When you get the tools list from the MCP server, you can filter it before passing it to each `AssistantAgent` constructor. This lets you create specialists that only have access to certain functions, forcing them to collaborate.

The server only sees the inputs for a single tool call, like a wake-up time. The tool's output is then passed back into your AutoGen chat. Data is processed ephemerally by Vinkius for each call and isn't stored or shared between them on the server side.

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