How to Use the Unbounce MCP in LlamaIndex

Q: Can I query Unbounce structures using LlamaIndex?

Absolutely. You can index the output from listdomains or listaccounts. This lets you ask complex, natural language questions that are answered by combining multiple pieces of data.

Q: What specific data types does the Unbounce MCP Server touch?

The server handles domain records (listdomains), page metadata (getpage), and lead details (getlead).

Build RAG applications by indexing Unbounce data with LlamaIndex.

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Connect Unbounce MCP to LlamaIndex

Create your Vinkius account to connect Unbounce 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 Unbounce with LlamaIndex

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Search Past Campaign Performance

LlamaIndex lets you index the results from `list_leads` into a vector store. Instead of just getting a list, your agent can perform semantic searches across all past lead data. Need to remember what happened last quarter? Querying this indexed data gives grounded answers about specific leads or pages that match your query, not hallucinations.

Index Unbounce Account Structures

Use `list_accounts` and `list_sub_accounts`, then index the results. This builds a knowledge base of every account structure you manage. Your developer can combine this live API data with internal documents. The resulting RAG application answers questions like, 'Which sub-account manages domains listed in X?'

Query Page and Form Field Definitions

First, call `get_page` to fetch a page's details. Then, use `list_form_fields` and index those fields. This allows you to query the exact purpose or structure of any form on any landing page. This knowledge base is great for documentation. You can ask: 'What are the required fields for sign-ups on my primary conversion page?'

Setup guide

Set up Unbounce 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 Unbounce 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 Unbounce tools.",
)
response = await agent.run("List recent Unbounce data")

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Common questions about Unbounce MCP in LlamaIndex

LlamaIndex indexes the results from MCP tool calls, turning raw API data into a searchable knowledge base. This means your AI client can retrieve and ground answers using actual `get_page` or `list_leads` data.

Absolutely. You can index the output from `list_domains` or `list_accounts`. This lets you ask complex, natural language questions that are answered by combining multiple pieces of data.

It supports structured operational data. You can index lists of pages (`list_pages`), lead records (`list_leads`), and account details, making them all part of your searchable knowledge.

Yes, the server output is designed to be consumable by RAG architectures. The client ingests tool outputs and turns them into persistent, queryable indexes for semantic search.

The server handles domain records (`list_domains`), page metadata (`get_page`), and lead details (`get_lead`).

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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