How to Use the OpenTHC MCP in Pydantic AI

Q: What's the setup process for using the OpenTHC server with Pydantic AI?

It's minimal. Install the library, then instantiate an MCPToolset with your unique Vinkius URL. Pass this toolset into your Agent's constructor. Pydantic AI automatically makes all the OpenTHC tools available to your agent.

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Setup OpenTHC with Pydantic AI

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Guaranteed Data Correctness

Pydantic AI's main job is to prevent bad data from corrupting your agent's state. When your agent calls `list_lab_results`, Pydantic AI validates the entire response against a strict schema. If the lab result is missing a required field, your agent gets a `ValidationError` immediately. This means no more defensive coding or manually checking for `None`. Your agent can confidently access data from tools like `list_products` or `list_inventory`, knowing that if the code proceeds, the data structure is exactly what it expects. It's about writing less, more reliable code.

Build Model-Agnostic Compliance Agents

Use OpenAI, Anthropic, Gemini, or a local model—it doesn't matter to Pydantic AI. The framework separates the LLM from the tools. This OpenTHC server provides the compliance functions, and you can swap the underlying model without rewriting your tool logic. Your agent's ability to, for example, use `list_plants` to check cultivation status or `list_contacts` to find a partner is independent of the LLM. This lets you choose the best model for the job without getting locked into one provider.

A Reliable MCP Server for Pydantic AI

This server exposes 12 essential OpenTHC functions. You can track your entire operation, from `list_varieties` to define your strains, to `list_sections` to map your facility, all the way to `list_b2c_transactions` at the point of sale. Integrating it is one line of code. You create an `MCPToolset` with your Vinkius server URL and add it to your agent. Pydantic AI handles the HTTP requests and response parsing, so you can focus on your agent's logic, not the plumbing.

Setup guide

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

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

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

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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 OpenTHC. 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 OpenTHC MCP in Pydantic AI

It validates every server response against a Pydantic model at runtime. If the OpenTHC server sends data—say, from `list_inventory`—that doesn't match the expected schema, Pydantic AI will raise a `ValidationError` instantly, preventing your agent from processing corrupt data.

It's minimal. Install the library, then instantiate an `MCPToolset` with your unique Vinkius URL. Pass this toolset into your `Agent`'s constructor. Pydantic AI automatically makes all the OpenTHC tools available to your agent.

Yes. Pydantic AI is model-agnostic. As long as you have an adapter for your local model, you can use it to drive agents that call any of the OpenTHC tools, like `list_plants` or `list_b2b_transactions`.

The error traceback will show you exactly which field failed validation and why. For example, it might say a `license_expiration_date` from the `list_licenses` tool was expected to be a string but was `null`. This points you directly to the data issue.

Your data is secure. The connection uses your private Vinkius token. The server itself is ephemeral; it processes requests for your OpenTHC plant, inventory, and transaction data and then forgets. No compliance data is logged or stored by Vinkius.

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