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Indy MCP Server for Pydantic AIGive Pydantic AI instant access to 12 tools to Create Form, Create Webhook, Delete 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 Indy 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 Indy 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 Indy "
            "(12 tools)."
        ),
    )

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

asyncio.run(main())
Indy
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
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 Indy MCP Server

Connect your Indy account to any AI agent and manage forms and records through natural conversation.

Pydantic AI validates every Indy 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.

What you can do

  • Form Management — List all forms, inspect configurations, create new forms, and delete unused ones
  • Record Tracking — Browse all form submissions, inspect individual records with full field data
  • Template Management — List and inspect form templates for reusable designs
  • Group Organization — Browse form groups for organized management
  • File Access — List files attached to form submissions

The Indy 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 Indy tools available for Pydantic AI

When Pydantic AI connects to Indy through Vinkius, your AI agent gets direct access to every tool listed below — spanning form-builder, data-collection, freelance-management, 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_form

Create a new form

create_webhook

Create a new webhook

delete_form

Delete a form

delete_webhook

Delete a webhook

get_account_info

Get account status

get_form

Get form details

get_record

Get submission details

get_user

Get user details

list_forms

List all forms

list_records

List form submissions

list_users

List connected users

list_webhooks

List active webhooks

Connect Indy to Pydantic AI via MCP

Follow these steps to wire Indy 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 Indy with type-safe schemas

Why Use Pydantic AI with the Indy MCP Server

Pydantic AI provides unique advantages when paired with Indy 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 Indy 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 Indy connection logic from agent behavior for testable, maintainable code

Indy + Pydantic AI Use Cases

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

01

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

02

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

03

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

04

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

Example Prompts for Indy in Pydantic AI

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

01

"Show all forms and the latest submissions for the 'Customer Feedback' form."

02

"Create a new 'Event Registration' form and list available templates."

03

"Show all records for the bug report form and any attached files."

Troubleshooting Indy MCP Server with Pydantic AI

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

01

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Indy + Pydantic AI FAQ

Common questions about integrating Indy 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 Indy MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.