How to Use the Kajabi MCP in LlamaIndex

Q: How do I index Kajabi blog posts with LlamaIndex?

Use the listblogposts tool to fetch your articles, then pass the output to your LlamaIndex document parser. This indexes your marketing content for semantic retrieval during user chats.

Q: Can LlamaIndex update Kajabi contact tags?

Yes, your LlamaIndex agent can run write operations like addtagtocontact or removetagfromcontact. The agent decides to trigger these tools based on semantic triggers found in your user's chat history.

Q: How do I connect the Kajabi MCP Server to LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Vinkius URL. Convert the server's tools using McpToolSpec and pass them to your FunctionAgent constructor.

Q: Can I filter Kajabi customer lists in LlamaIndex?

Yes, you query listcustomers using a specific site ID obtained from listsites. LlamaIndex processes the resulting customer array, allowing you to build targeted vector stores for specific user segments.

Q: How is my Kajabi purchase history data protected?

Sensitive purchase details retrieved via listpurchases and listorders are processed inside an ephemeral V8 isolate on Vinkius. Your API keys are stored securely, and the data is never cached or used to train public models.

Index live Kajabi course and contact data into LlamaIndex vector stores for hallucination-free RAG applications.

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Connect Kajabi MCP to LlamaIndex

Create your Vinkius account to connect Kajabi to LlamaIndex 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 LlamaIndex

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Index Kajabi course contents into LlamaIndex vector stores

The `list_courses` tool retrieves your active curriculum so LlamaIndex can index it for semantic search. Your RAG pipelines call `get_course_details` to pull specific lesson structures, turning raw API payloads into searchable vector nodes. This ensures your customer support agent answers queries using live course data, not outdated training sets. LlamaIndex ingest pipelines process these tool outputs directly, updating your local index whenever course details change. Your agent queries this index to resolve user questions about your curriculum.

Ground customer support in real Kajabi contact records

The `get_contact_details` tool provides the exact state of a user's profile to your LlamaIndex query engines. By calling `list_contacts` and feeding the output into a vector index, your agent checks user progress and tags before answering questions. This keeps your agent's responses grounded in actual user records rather than guesses. You can combine this with `list_tags` to segment search results. The agent filters its knowledge base based on the tags retrieved from the contact's profile.

Query Kajabi offer details using this MCP Server

The `list_offers` tool pulls pricing and packaging structures directly into your LlamaIndex data connectors. Your agent uses `get_offer_details` to extract specific terms, indexing them alongside your product documentation. This lets your pipeline resolve complex pricing queries by cross-referencing live offers with customer questions. Running this through the Vinkius MCP Server means your LlamaIndex application doesn't need custom API wrapper code. The server handles all transport and schema formatting automatically.

Setup guide

Set up Kajabi MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Kajabi MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Kajabi tools.",
)
response = await agent.run("List recent Kajabi data")

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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 LlamaIndex

Use the `list_blog_posts` tool to fetch your articles, then pass the output to your LlamaIndex document parser. This indexes your marketing content for semantic retrieval during user chats.

Yes, your LlamaIndex agent can run write operations like `add_tag_to_contact` or `remove_tag_from_contact`. The agent decides to trigger these tools based on semantic triggers found in your user's chat history.

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient` with your Vinkius URL. Convert the server's tools using `McpToolSpec` and pass them to your `FunctionAgent` constructor.

Yes, you query `list_customers` using a specific site ID obtained from `list_sites`. LlamaIndex processes the resulting customer array, allowing you to build targeted vector stores for specific user segments.

Sensitive purchase details retrieved via `list_purchases` and `list_orders` are processed inside an ephemeral V8 isolate on Vinkius. Your API keys are stored securely, and the data is never cached or used to train public models.

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