How to Use the Typeform MCP in Pydantic AI

Q: How does Pydantic AI connect to Typeform?

The MCP Server exposes tools like listforms and getformdetails. Your agent discovers these functions, but critically, it wraps the execution to ensure the returned data structure matches your desired Python models.

Q: Can I manage multiple Typeform workspaces with Pydantic AI?

Yes. You can use listworkspaces to get a list of all available contexts. The agent processes this list, ensuring that subsequent operations on forms within those workspaces are correctly validated.

Get guaranteed, validated data from your Typeform forms using Pydantic AI.

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Connect Typeform MCP to Pydantic AI

Create your Vinkius account to connect Typeform 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 Typeform with Pydantic AI

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Validate Form Responses with the MCP Server

The biggest headache in API calls is unexpected fields. When you run `get_form_responses`, the agent uses Pydantic to validate the payload against a defined model at runtime. If Typeform changes its response structure, your agent fails loudly—you know exactly what went wrong. This prevents silent corruption of data. You don't get hallucinated fields; you get a clear, Python-enforced validation error immediately.

Inspect Form Layouts using Typeform

Before writing logic that relies on form structure, the agent calls `get_form_details`. This ensures your code is robust. Since it validates the output, you know exactly how many fields to expect and what data type they should hold. This level of strictness means you're building production-grade software, not just testing a prototype.

List Available Themes for Typeform

Need to know the visual options? Running `list_form_themes` pulls all available themes into your agent. Because Pydantic validates this list, you get a predictable array of theme names that your code can safely iterate over. It’s simple data retrieval, but the validation layer gives you absolute confidence in the returned structure.

Setup guide

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

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

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

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Common questions about Typeform MCP in Pydantic AI

The MCP Server exposes tools like `list_forms` and `get_form_details`. Your agent discovers these functions, but critically, it wraps the execution to ensure the returned data structure matches your desired Python models.

Typeform provides the source data—the forms and their submissions. The MCP Server acts as the secure, validated conduit between Typeform's API and your agent's structured Python code.

Yes. You can use `list_workspaces` to get a list of all available contexts. The agent processes this list, ensuring that subsequent operations on forms within those workspaces are correctly validated.

Yes. That’s the whole point. Every piece of data—whether it's a form ID or a submission record—is checked against your schema, eliminating unexpected API behavior.

This server touches Form Responses. The agent guarantees that the raw submission payloads are returned and validated against a clean Pydantic model before your code sees them.

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