How to Use the Breathing Timer MCP in LlamaIndex

Q: Can LlamaIndex help manage historical Breathing Timer usage?

Yes. You can store previous outputs from generatebreathingschedule into a vector store, allowing you to query past sessions for specific details or trends.

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Connect Breathing Timer MCP to LlamaIndex

Create your Vinkius account to connect Breathing Timer to LlamaIndex 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 LlamaIndex

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LlamaIndex: Indexing Timing Parameters

Don't just run the tools, save them. When you use `query_breathing_parameters`, the output is indexed into your knowledge base. This means you can later query past sessions or configurations and get answers grounded in actual timing data. This ability turns live API calls into a searchable index for your RAG applications.

LlamaIndex: Querying Past Breathing Schedules

You can use `generate_breathing_schedule` and then store the results. Later, you query the indexed data to retrieve specific schedules that were run weeks ago. LlamaIndex combines this historical API data with your documents for unified searching. This is far better than relying on memory or simple logging.

LlamaIndex: Calculating and Storing Cycle Durations

The results from `calculate_single_cycle_duration` become valuable, searchable facts. By indexing these measured durations, you can answer questions like 'What was the typical cycle time for 4-7-8?' based on logged data. This gives your knowledge graph measurable context derived directly from the MCP.

Setup guide

Set up Breathing Timer MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Breathing Timer MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Breathing Timer tools.",
)
response = await agent.run("List recent Breathing Timer data")

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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 LlamaIndex

LlamaIndex indexes the results of this MCP. Instead of just executing a tool, you capture its output—like a calculated schedule or parameter set—and turn it into searchable knowledge.

The primary data indexed are the structured timing parameters of breathing cycles. This includes specific duration metrics from techniques like Box Breathing and 4-7-8.

Yes. You can store previous outputs from `generate_breathing_schedule` into a vector store, allowing you to query past sessions for specific details or trends.

The MCP supports various structured cycles, including Coherence Heart Rate. You can index data from all these different techniques into one unified knowledge base.

The server touches timing parameters. These are structured measurements detailing the duration of breathing cycles, not personal identity information.

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