How to Use the OpnForm MCP in Pydantic AI

Q: How does Pydantic AI connect to this OpnForm MCP server?

It's straightforward. You import MCPToolset and initialize it with your Vinkius server URL, like MCPToolset('http://...'). Then you pass it into your Agent's toolsets list.

Q: What happens if the OpnForm API returns unexpected data when I'm using Pydantic AI?

Pydantic AI will raise a ValidationError immediately. This is its main strength. It protects your application from processing malformed data from tools like getsubmission provided by the MCP server, preventing bugs down the line.

Build correct and type-safe agents that interact with OpnForm, validated at runtime by Pydantic AI.

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Connect OpnForm MCP to Pydantic AI

Create your Vinkius account to connect OpnForm to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup OpnForm with Pydantic AI

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Get Schema-Validated Form Data

Stop worrying about API drift or unexpected `null` values. When your agent calls `get_submission` or `list_forms`, Pydantic AI automatically parses the JSON response into a Pydantic model. If the data doesn't match the expected schema—maybe a field name changed or a type is wrong—your code raises a `ValidationError` instantly. No silent data corruption. You get reliable data objects every time.

Construct Forms with Confidence

Building forms programmatically can be a mess of nested dictionaries. With Pydantic AI, you construct the form using typed Pydantic models first. It's clean, your IDE gives you autocomplete, and you know the structure is valid before you even send it. Then, you just pass that model to the `create_form` or `update_form` tool. The framework handles the serialization for you. This makes managing complex forms with conditional logic much less error-prone.

A Model-Agnostic OpnForm MCP Server

This MCP Server for OpnForm works with any LLM that Pydantic AI supports. You aren't locked into one provider. Use OpenAI, Anthropic, Gemini, or a local model running on your own machine. The tool-calling logic is handled by Pydantic AI, which acts as a universal adapter. Your code for interacting with `get_form` or `list_workspaces` stays the same, no matter which backend LLM you choose.

Setup guide

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

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

result = await agent.run("List recent OpnForm 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 OpnForm. 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 OpnForm MCP in Pydantic AI

It's straightforward. You import `MCPToolset` and initialize it with your Vinkius server URL, like `MCPToolset('http://...')`. Then you pass it into your `Agent`'s `toolsets` list.

Pydantic AI will raise a `ValidationError` immediately. This is its main strength. It protects your application from processing malformed data from tools like `get_submission` provided by the MCP server, preventing bugs down the line.

Yes, and it's the best way to do it. You define the form's structure using Pydantic models, which gives you type-checking and autocompletion. Then you pass the validated model to the `create_form` tool.

No, you can use any model Pydantic AI supports. The framework abstracts the model-specific function-calling logic. Your code for using the OpnForm tools remains the same whether you're using OpenAI or a local Llama model.

This server handles your form schemas and submission content. Pydantic AI adds a critical security layer by performing runtime data validation. It acts like a firewall, ensuring that any data from `get_submission` or `get_form` strictly conforms to your defined Pydantic models before it ever reaches your application logic.

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