How to Use the Breathing Timer MCP in AutoGen

Q: Can AutoGen use Breathing Timer to compare different routines?

Yes. Agents can run multiple checks, calling generatebreathingschedule and then comparing the output against parameters from querybreathingparameters before recommending a final choice.

Let agents debate optimal respiratory routines with AutoGen's Breathing Timer MCP tools.

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Setup Breathing Timer with AutoGen

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AutoGen: Consensus-Driven Scheduling

You can set up a debate where one agent proposes a schedule using `generate_breathing_schedule`. A second, critical agent then challenges that proposal by running `query_breathing_parameters` to check the fundamental timing rules. The agents negotiate until they agree on the optimal routine. This forces deliberation before execution.

AutoGen: Validating Cycle Metrics

If the initial debate settles on a technique, a third agent can validate the actual time required by calling `calculate_single_cycle_duration`. This confirms whether the proposed routine is physically possible and accurate before proceeding. The consensus is built on verified metrics. It's how you ensure the answer isn't obvious.

AutoGen: Multi-Agent Health Assessment

The MCP allows agents to coordinate complex tasks. One agent might use `query_breathing_parameters` to understand the limits of Coherence Heart Rate, while another uses that knowledge to build a schedule with `generate_breathing_schedule`. They pass constraints back and forth until the system reaches a validated final plan. This mimics how real experts collaborate.

Setup guide

Set up Breathing Timer 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 Breathing Timer 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="Breathing Timer_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

The MCP provides specific, quantifiable tools that agents must agree on. They use the timing functions to debate and converge on a single, verifiable breathing routine.

The core data points are structured timing parameters. The agents discuss cycle durations, default timings for techniques like 4-7-8, and total required scheduling time.

Yes. Agents can run multiple checks, calling `generate_breathing_schedule` and then comparing the output against parameters from `query_breathing_parameters` before recommending a final choice.

The goal is consensus-driven decision making. The agents don't just suggest; they argue and verify the feasibility of the breathing routine using the MCP tools.

It does. The system is designed to handle various structured cycles, allowing agents to discuss and agree upon which technique best meets a user's current needs.

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