How to Use the Kajabi MCP in Pydantic AI

Q: How do I configure Kajabi with Pydantic AI?

Install the pydantic-ai-slimmcp package. Create an MCPToolset pointing to your HTTP endpoint. Pass that toolset array directly to your Agent initialization block.

Q: Will Pydantic AI catch invalid Kajabi tag IDs?

Yes. If the agent attempts to pass malformed data to removetagfromcontact, Pydantic intercepts the payload. The script raises a validation exception before the API call executes.

Q: Why is the framework throwing an error on my product list?

Your agent likely received unexpected fields from listproducts. Pydantic enforces strict schema matching, so any deviation from your defined model results in an immediate runtime failure.

Q: How secure is my course and offer data?

We handle requests for getcoursedetails through isolated, single-use V8 sandboxes. Your Vinkius token acts as a temporary bridge, and we drop the connection the millisecond the payload delivers.

Enforce strict typing on your Kajabi data using Pydantic AI and this MCP Server to validate every contact.

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Connect Kajabi MCP to Pydantic AI

Create your Vinkius account to connect Kajabi 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 Kajabi with Pydantic AI

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Perplexity

Type-Safe Contact Updates

The `add_tag_to_contact` tool requires exact IDs to function correctly. Pydantic AI validates the inputs before the request even leaves your server, ensuring you never pass a string when the API expects an integer. If the agent tries to invent a tag ID that doesn't exist, the framework throws a massive validation error. Your script crashes immediately instead of failing silently or corrupting your marketing lists.

Validated Pydantic AI Models

Tools like `get_product_details` and `get_offer_details` return complex nested JSON structures. You define strict Pydantic models in your code to map exactly what fields you expect from the MCP Server. When the data arrives, the framework checks every single key. If the platform changes an API response format, your agent catches the mismatch at runtime rather than hallucinating prices in production.

Multi-Site Schema Routing

The `list_sites` tool extracts the unique identifiers required to route queries. Your agent uses these validated IDs to run `list_orders` across different creator properties. Because the framework is model-agnostic, you can swap out the underlying LLM at any time. The strict type checking guarantees that an open-source local model interacts with your storefront exactly like Anthropic or OpenAI would.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Kajabi. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Kajabi MCP in Pydantic AI

Install the `pydantic-ai-slim[mcp]` package. Create an `MCPToolset` pointing to your HTTP endpoint. Pass that toolset array directly to your Agent initialization block.

Yes. If the agent attempts to pass malformed data to `remove_tag_from_contact`, Pydantic intercepts the payload. The script raises a validation exception before the API call executes.

You absolutely can. Pydantic AI abstracts the provider logic entirely. A local model can call `list_contacts` and parse the results as long as it adheres to your defined schemas.

Your agent likely received unexpected fields from `list_products`. Pydantic enforces strict schema matching, so any deviation from your defined model results in an immediate runtime failure.

We handle requests for `get_course_details` through isolated, single-use V8 sandboxes. Your Vinkius token acts as a temporary bridge, and we drop the connection the millisecond the payload delivers.

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