How to Use the NeetoCal MCP in Pydantic AI

Q: How do I configure NeetoCal with Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset instance with your HTTP endpoint URL. Pass this toolset into your Agent constructor. Do not use the deprecated MCPServerHTTP class.

Q: Why use Pydantic AI for NeetoCal over a standard HTTP client?

It guarantees the shape of your scheduling data. If getbooking returns an unexpected JSON structure, the framework throws a validation error immediately. Your agent will never hallucinate a calendar slot based on corrupted data.

Type-safe NeetoCal scheduling for Pydantic AI agents that demand strict runtime validation.

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Connect NeetoCal MCP to Pydantic AI

Create your Vinkius account to connect NeetoCal 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 NeetoCal with Pydantic AI

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Type-Safe NeetoCal MCP Server

This MCP Server delivers `create_booking` and `get_slots` capabilities to Pydantic AI with absolute type safety. When your agent asks for available times, the framework validates the API response against strict Pydantic models. If the scheduling backend returns a malformed date string, the system fails loudly instead of passing bad data to your user. This exactness matters when you build production systems. Your agent calls `get_availability` and knows the resulting data structure is guaranteed. You spend zero time writing defensive parsing logic for calendar payloads.

Model-Agnostic Modifications

Managing existing appointments relies on the `get_booking`, `reschedule_booking`, and `cancel_booking` tools. A user asks to move their 3 PM meeting to Thursday. The agent fetches the current details, validates the new time slot, and executes the change. Because the framework is model-agnostic, you can swap between Anthropic, OpenAI, or local models without rewriting your scheduling logic. The tool inputs remain consistent. The agent simply passes the required parameters to the endpoint and waits for the typed response.

Link Routing & Health Checks

Agents distribute meeting access using `get_scheduling_link` and `list_scheduling_links`. They pull the correct URL for a specific event type and hand it to the user. You can chain this with `list_calendars` to verify which accounts are currently active. Connection health is verified via `check_neetocal_status`. You set up the integration using the unified `MCPToolset` class, passing your HTTP endpoint URL. The agent handles the SSE or Streamable HTTP transport layer under the hood.

Setup guide

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

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

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

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Common questions about NeetoCal MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Create an `MCPToolset` instance with your HTTP endpoint URL. Pass this toolset into your `Agent` constructor. Do not use the deprecated `MCPServerHTTP` class.

It guarantees the shape of your scheduling data. If `get_booking` returns an unexpected JSON structure, the framework throws a validation error immediately. Your agent will never hallucinate a calendar slot based on corrupted data.

Yes. The framework is completely model-agnostic. You can connect a local Llama 3 model to the scheduling tools, and it will execute `list_availabilities` exactly like GPT-4 would.

The unified toolset supports both Streamable HTTP and SSE transports. You just provide the URL, and the framework negotiates the connection to the external server.

The tools read and write meeting times, participant emails, and calendar IDs. We isolate every request inside a V8 sandbox. You authenticate via a single endpoint token, keeping raw API keys out of your application code.

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