How to Use the MyTime MCP in Pydantic AI

Use MyTime with Pydantic AI for type-safe business operations that fail loud when data doesn't match your schema.

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Connect MyTime MCP to Pydantic AI

Create your Vinkius account to connect MyTime 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 MyTime with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Strictly validated scheduling

Every response from `get_appointment` is parsed against your Pydantic models. If the API returns a malformed date or an unexpected field, the agent stops immediately. This prevents silent errors from propagating into your business logic. You get a clean, validated object every time your agent completes a lookup.

Customer record verification

Your agent uses `list_customers` to pull contact info and maps it directly to your internal data classes. The runtime validation ensures that every customer record is complete and usable. It removes the need for manual parsing or custom cleanup scripts. Your code interacts with typed data that is guaranteed to match your expectations.

Reliable staff data pipelines

Fetch your staff roster with `list_staff` and trust the result because it's validated at the edge. The agent rejects any response that lacks required fields or contains invalid types. You build your logic on a stable foundation. If the API structure changes, your agent catches it during the next request rather than crashing later.

Setup guide

Set up MyTime 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": {
        "mytime-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about MyTime MCP in Pydantic AI

It provides runtime validation for every tool response. This ensures your agent never acts on corrupted or hallucinated data retrieved from your business account.

Yes, it supports both Streamable HTTP and SSE transports. You can configure your connection easily using the `MCPToolset` class.

The framework will throw a validation error immediately. This forces the agent to handle the discrepancy rather than proceeding with bad data.

The server is model-agnostic. You can swap between different LLM providers while keeping your same Pydantic validation logic for all tools.

Data is validated in-memory during the request cycle and never stored permanently by the agent. Your customer names and appointment details are handled strictly within the type-checked scope of your application.

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