How to Use the Cronofy MCP in Pydantic AI

Q: What happens if the Cronofy API changes when using Pydantic AI?

Your agent will fail immediately with a ValidationError. This is a feature, not a bug. It prevents your application from silently breaking and ensures you're always working with data that matches your defined Pydantic models.

Q: How do I connect Cronofy to my Pydantic AI agent?

Just pip install "pydantic-ai-slimmcp", then create an MCPToolset with your Vinkius URL. Add the toolset to your agent's constructor, and all the tools are ready and type-checked.

Add type-safe calendar automation to any Python app. Use Cronofy with Pydantic AI for scheduling that never fails silently.

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Connect Cronofy MCP to Pydantic AI

Create your Vinkius account to connect Cronofy 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 Cronofy with Pydantic AI

Ask AI about this MCP

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Claude

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Stop Guessing API Responses

The `read_events` tool fetches calendar events, and `get_user_info` pulls user details. You don't just get a JSON blob; you get a Pydantic model. Every response is validated on the spot. If Cronofy's API ever returns an unexpected field or the wrong data type, Pydantic AI throws a `ValidationError`. This means your application code never has to deal with corrupted calendar data. It either works correctly or it fails loudly.

Reliably Manage Events with Pydantic AI

Use the `create_event` and `delete_event` tools to manage calendar entries. Pydantic AI validates the inputs you send and the responses you get back. This makes your scheduling agent incredibly robust. You know for a fact that when your code calls `create_event`, the request is properly formed. It's the right way to build if correctness is your top priority.

Query Availability with an MCP Server

The `query_availability` and `get_free_busy` tools let your agent find open time slots. Pydantic AI ensures the list of available times your agent receives matches the exact data structure you defined. No more writing defensive code to check if a time slot is a string or a datetime object. With Pydantic AI and this MCP server, it's guaranteed to be correct. You can focus on your application's logic, not on parsing API responses.

Setup guide

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

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

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

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Common questions about Cronofy MCP in Pydantic AI

Pydantic AI automatically validates every API call to and from the Cronofy server against your Python type hints. If the `read_events` tool returns data in an unexpected format, your code will raise an error instead of processing bad data.

Yes. Pydantic AI is model-agnostic, so it works with OpenAI, Anthropic, Gemini, or models running on your own machine. Just connect the Cronofy MCP Server and your chosen LLM.

Your agent will fail immediately with a `ValidationError`. This is a feature, not a bug. It prevents your application from silently breaking and ensures you're always working with data that matches your defined Pydantic models.

Just `pip install "pydantic-ai-slim[mcp]"`, then create an `MCPToolset` with your Vinkius URL. Add the toolset to your agent's constructor, and all the tools are ready and type-checked.

The server handles your users' calendar events, availability, and profile information. Pydantic AI adds a layer of data integrity, and Vinkius secures the transport layer, ensuring that this sensitive scheduling data is both correct and protected.

Use it with your favorite AI tools

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