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Proforms MCP Server for Pydantic AIGive Pydantic AI instant access to 12 tools to Create Job, Get Asset, Get Form, and more

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Proforms through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Ask AI about this App Connector for Pydantic AI

The Proforms app connector for Pydantic AI is a standout in the Productivity category — giving your AI agent 12 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Proforms "
            "(12 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Proforms?"
    )
    print(result.data)

asyncio.run(main())
Proforms
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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Proforms MCP Server

The Proforms MCP server allows your AI agent to query form responses, retrieve submission data, and list active forms natively. Analyze your collected data immediately through conversation without downloading CSV files.

Pydantic AI validates every Proforms tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

The Proforms MCP Server exposes 12 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 12 Proforms tools available for Pydantic AI

When Pydantic AI connects to Proforms through Vinkius, your AI agent gets direct access to every tool listed below — spanning form-builder, data-collection, submission-tracking, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

create_job

Push a new job to a field worker

get_asset

Retrieve details for a specific asset

get_form

Retrieve details for a specific form

get_job

Retrieve details for a specific field job

get_me

Check API connectivity and get user context

get_submission

Retrieve details for a specific form submission

list_assets

List all registered equipment assets

list_forms

List all mobile forms

list_jobs

List all field jobs/tasks

list_submissions

List all data submissions for a specific form

list_users

List all back-office and field users

update_job

Modify an existing field job

Connect Proforms to Pydantic AI via MCP

Follow these steps to wire Proforms into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install Pydantic AI

Run pip install pydantic-ai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.py and run: python agent.py
04

Explore tools

The agent discovers 12 tools from Proforms with type-safe schemas

Why Use Pydantic AI with the Proforms MCP Server

Pydantic AI provides unique advantages when paired with Proforms through the Model Context Protocol.

01

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

02

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Proforms integration code

03

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

04

Dependency injection system cleanly separates your Proforms connection logic from agent behavior for testable, maintainable code

Proforms + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Proforms MCP Server delivers measurable value.

01

Type-safe data pipelines: query Proforms with guaranteed response schemas, feeding validated data into downstream processing

02

API orchestration: chain multiple Proforms tool calls with Pydantic validation at each step to ensure data integrity end-to-end

03

Production monitoring: build validated alert agents that query Proforms and output structured, schema-compliant notifications

04

Testing and QA: use Pydantic AI's dependency injection to mock Proforms responses and write comprehensive agent tests

Example Prompts for Proforms in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Proforms immediately.

01

"List all active forms in my account."

02

"Fetch the latest responses for form ID 8901."

03

"Summarize the feedback from 'Customer Feedback Survey'."

Troubleshooting Proforms MCP Server with Pydantic AI

Common issues when connecting Proforms to Pydantic AI through the Vinkius, and how to resolve them.

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Proforms + Pydantic AI FAQ

Common questions about integrating Proforms MCP Server with Pydantic AI.

01

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
02

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
03

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Proforms MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.