How to Use the Breathing Timer MCP in LangChain

Q: Can the Breathing Timer support complex routines with LangChain?

Yes. You can combine querybreathingparameters with generatebreathingschedule, allowing your agent to create and validate multi-stage breathing regimens based on user input.

Build multi-step reasoning pipelines for guided respiratory health using LangChain.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Breathing Timer MCP to LangChain

Create your Vinkius account to connect Breathing Timer to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Breathing Timer with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

LangChain: Building Reasoning Chains

Your agent decides the flow. Start by calling `query_breathing_parameters` to fetch default timing settings for any technique. This allows your chain to select appropriate data before attempting a cycle calculation. Next, you use `generate_breathing_schedule` when the user provides a total practice duration. The output of that schedule generation becomes the input needed by `calculate_single_cycle_duration`, letting your agent confirm precise timing for every segment.

LangChain: Dynamic Scheduling Logic

Need to adjust timing on the fly? After generating a structured breathing routine, you can pass those parameters into `calculate_single_cycle_duration`. This function returns the precise time needed for one full cycle. Your agent uses this measured output to validate or modify subsequent steps in the chain. This sequence lets your multi-agent system build highly accurate and verifiable health workflows.

LangChain: Structured Health Pipelines

The MCP makes chaining simple. You can link `query_breathing_parameters` to define the rules, then use those rules in `generate_breathing_schedule`. This creates a robust data pipeline that runs through your agent's logic. Since every tool call is observable via tracing, you see exactly how your agent determined the necessary timing for optimal respiratory practice.

Setup guide

Set up Breathing Timer MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Breathing Timer tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "breathing-timer-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Breathing Timer transactions"
    })
    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 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Breathing Timer now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Breathing Timer MCP in LangChain

LangChain uses this MCP to sequence steps. An agent can first query parameters, then build a schedule, and finally calculate precise cycle durations using the tools in your MCP.

The system tracks structured timing parameters for breathing cycles. This includes the duration of different phases like inhalation and exhalation, which are critical to respiratory health assessments.

Yes. You can combine `query_breathing_parameters` with `generate_breathing_schedule`, allowing your agent to create and validate multi-stage breathing regimens based on user input.

You'll want to manage state persistence using the client's session function. This allows your agent to remember previous schedules and use historical data for comparison or refinement.

Absolutely. The tools are built to handle recognized patterns like Box Breathing, 4-7-8, and Coherence Heart Rate, giving your agent options for various needs.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript