How to Use the Breathing Timer MCP in OpenAI Agents SDK

Guide users to calm using your AI client and OpenAI Agents SDK.

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Connect Breathing Timer MCP to OpenAI Agents SDK

Create your Vinkius account to connect Breathing Timer to OpenAI Agents SDK 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 Breathing Timer with OpenAI Agents SDK

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Schedule Training Sessions with MCP Server

You can build full intervention workflows by having the agent use `generate_breathing_schedule`. This tool takes a total time goal and maps out structured cycles, whether it's Box Breathing or 4-7-8. It gives you the necessary roadmap for guided respiratory practice. The guardrails in your OpenAI Agents SDK ensure that any generated schedule adheres to safety parameters before running. You get full tracing on the OpenAI dashboard, so you know exactly which steps your agent took and why.

Validate Cycle Timing via MCP Server

Need to confirm how long a pattern runs? The `calculate_single_cycle_duration` tool figures out the exact time for one full cycle. You pass in the technique and timing parameters, and it returns the total seconds needed. This helps you build reliable agents that don't guess at math. It keeps your production agent system stable, knowing exactly how long each segment needs to be.

Check Breathing Parameters with OpenAI Agents SDK

Before building a routine, check the default rules using `query_breathing_parameters`. This tool pulls the standard timing for any recognized breathing technique. It’s quick and gives you baseline data without needing to run a full schedule. This MCP Server capability lets your agent act as a knowledge base, instantly providing medical practitioners or wellness apps with correct protocol details.

Setup guide

Set up Breathing Timer MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Breathing Timer tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Breathing Timer tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Breathing Timer tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Breathing Timer Agent",
            instructions="You have access to Breathing Timer tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Breathing Timer MCP in OpenAI Agents SDK

It provides structured cycles like Box Breathing and 4-7-8, giving immediate guidance. The MCP Server tracks adherence metrics, helping your agent report on long-term habit formation.

Absolutely. You can chain this MCP Server capability with messaging or billing tools. Your agent uses a single connection to manage workflows across multiple platforms.

This server touches breathing cycle metadata, including structured timing parameters and adherence logs. All calls are cryptographically signed in a tamper-proof audit trail.

Yes. Vinkius manages this MCP Server inside an isolated V8 sandbox, enforcing over 34 security rules on every call. User credentials are only in transit and never stored.

It works great. You can have your agent first query parameters, then generate a schedule, all as part of one automated task flow.

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