How to Use the AidaForm MCP in Pydantic AI

Q: How do I connect Pydantic AI to the AidaForm MCP Server?

Install pydantic-ai-slimmcp and initialize an MCPToolset with your Vinkius HTTP endpoint. Pass that directly into the toolsets array when building your Agent.

Q: Can I list my AidaForm workspaces using Pydantic AI?

Yes. The agent calls listworkspaces and maps the output to your predefined Pydantic models. You get a clean, validated list of folders ready for programmatic navigation.

Connect the AidaForm MCP Server to Pydantic AI and force strict runtime validation on every survey response your agent touches.

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Connect AidaForm MCP to Pydantic AI

Create your Vinkius account to connect AidaForm 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 AidaForm with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Type-Safe Form Responses

The `get_response` and `list_responses` tools feed structured survey data into your Pydantic AI agent with absolute precision. If the AidaForm API returns a missing email field or an unexpected data type, the framework throws a loud validation error immediately. You never have to worry about silent corruption in your downstream systems. Because you define the exact Pydantic models for your expected survey answers, the agent guarantees that every piece of data matches your schema before processing it further.

Reliable AidaForm MCP Server Management

Tools like `create_form` and `update_form` execute exactly as intended when backed by strict type checking. Your agent constructs the form payload, validates it against your internal rules, and only sends the request when the structure is flawless. This model-agnostic setup means you swap between Claude, OpenAI, or local models without rewriting your tool logic. The `MCPToolset` handles the translation, while `list_forms` verifies that the new survey actually exists in the target account.

Analytics Without Hallucinations

Calling `get_form_analytics` pulls conversion metrics and completion rates straight from the source. LLMs love to invent numbers when summarizing data, but Pydantic AI forces the agent to ground its answers in the exact integers returned by the API. If you want to clean up old data, the `delete_form` and `delete_response` tools are ready to go. You write strict dependency injection rules that require administrative approval models before the agent is allowed to drop records.

Setup guide

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

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

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

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Common questions about AidaForm MCP in Pydantic AI

Install pydantic-ai-slim[mcp] and initialize an MCPToolset with your Vinkius HTTP endpoint. Pass that directly into the toolsets array when building your Agent.

Runtime validation changes everything. If a user submits a malformed survey response, your agent catches the schema mismatch instantly instead of crashing your database later.

Yes. The agent calls list_workspaces and maps the output to your predefined Pydantic models. You get a clean, validated list of folders ready for programmatic navigation.

It works perfectly. The framework is completely model-agnostic, so as long as your local LLM supports basic tool calling, it can read responses and create forms.

Your MCP endpoint runs in a strict, ephemeral V8 Isolate. The moment your agent finishes pulling user contact details or survey answers, the sandbox is destroyed. We never log your payload data.

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