How to Use the Typeform MCP in Pydantic AI

Q: What is the best way to list Typeform workspaces using Pydantic AI?

Use listworkspaces. It pulls an account-wide index of all your form collections, helping you pinpoint where related forms live.

Q: How do I get details for a specific Typeform workspace with Pydantic AI?

The agent uses getworkspacedetails. This provides context on the entire grouping of forms within that particular workspace.

Q: Does the MCP Server help me pull collected responses with Typeform and Pydantic AI?

Yes. The listresponses tool pulls submitted data, which your agent then validates against defined Pydantic models for guaranteed correctness.

Q: How do I list all available design themes for Typeform and Pydantic AI?

The listdesignthemes function fetches a complete list of available visual templates. This is useful metadata for building or updating your forms.

Validate Typeform Data for Pydantic AI: Ensure correctness when reading form submissions with your agent.

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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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Retrieve Form Submission Data via the MCP Server

The tool uses `list_responses` to pull all submitted data, ensuring that every field is validated against a Pydantic model. You can filter these results by date or status. When you need an inventory of forms, running `list_forms` provides the necessary IDs.

Understand Form Structure and Metadata

To know exactly what a Typeform asks, call `get_form_details`. This gives your agent the full question structure before processing any answers. Furthermore, you can list all available design options using `list_design_themes`.

Manage Form Submission Channels with the MCP Server

You don't have to manually check submissions. The agent can manage webhooks by calling `create_webhook`, setting up real-time alerts for Typeform submissions. Alternatively, use `list_webhooks` to audit every webhook configured on a specific form.

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-alternative-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

Use `list_workspaces`. It pulls an account-wide index of all your form collections, helping you pinpoint where related forms live.

The agent uses `get_workspace_details`. This provides context on the entire grouping of forms within that particular workspace.

Yes. The `list_responses` tool pulls submitted data, which your agent then validates against defined Pydantic models for guaranteed correctness.

The `list_design_themes` function fetches a complete list of available visual templates. This is useful metadata for building or updating your forms.

This server touches form submission responses, which are user-provided answers. The `list_responses` tool accesses this completion data, ensuring your agent validates it accurately.

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