How to Use the Breathing Timer MCP in Pydantic AI

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Connect Breathing Timer MCP to Pydantic AI

Create your Vinkius account to connect Breathing Timer to Pydantic AI 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 Pydantic AI

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Generating Schedules with MCP Server

Use `generate_breathing_schedule` to reliably build structured training schedules. The agent accepts total time goals and returns a validated sequence of cycles for guided practice. This ensures that when your Pydantic AI client passes the results, they match expected schemas exactly. You avoid silent corruption because the response is type-checked at runtime.

Validating Cycle Timing with Pydantic AI

Before an agent executes a timing sequence, it can call `calculate_single_cycle_duration`. This tool precisely calculates one full cycle's duration for any given technique. This rigorous validation prevents runtime errors. If the math is off, your agent fails loud and clearly with a Pydantic validation error.

Checking Protocols using MCP Server

To get basic timing data, use `query_breathing_parameters`. It checks default timing for techniques like Coherence Heart Rate. This is the simplest way to confirm protocol settings. This capability makes your agent highly reliable by providing immediate, structured knowledge that conforms perfectly to your defined Python models.

Setup guide

Set up Breathing Timer MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "breathing-timer-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Breathing Timer tools.",
)

result = await agent.run("List recent Breathing Timer transactions")
print(result.output)

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Common questions about Breathing Timer MCP in Pydantic AI

It ensures every tool call returns data validated against a specific schema. You don't have to worry about hallucinated fields or unexpected formats, since the response must adhere to your Pydantic models.

Yes. It handles various structured cycles including Box Breathing and 4-7-8. The tools are designed to be flexible, but the output remains strictly typed.

The server touches breathing cycle metadata, specifically structured timing parameters and adherence logs. All calls generate a cryptographically signed audit trail.

Yes. The entire process runs through Vinkius's zero-trust proxy, meaning your API keys are used only in transit and never stored on disk.

It does. You can chain multiple tools together—for example, querying parameters then generating a schedule—and the final output will still be validated against your desired schema.

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