How to Use the Hotjar MCP in LlamaIndex

Q: Can I restrict which Hotjar tools LlamaIndex accesses?

Yes. You pass an allowedtools filter when setting up the spec. This prevents the agent from running listsites if it only needs survey data.

Q: How does LlamaIndex handle Hotjar heatmap data?

It converts the structured JSON from getheatmap into text nodes. You then embed these nodes to search for specific click patterns later.

Index Hotjar feedback and session recordings into your LlamaIndex vector store to build queryable UX knowledge bases.

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Connect Hotjar MCP to LlamaIndex

Create your Vinkius account to connect Hotjar 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 Hotjar with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Index qualitative feedback

Stop manually reading open-ended survey answers. Your script calls `list_survey_responses` and feeds the text directly into a LlamaIndex document store. Now you query the index to find out why people hate the new checkout flow. The system retrieves exact quotes from `get_survey` instead of making up generic complaints.

LlamaIndex MCP Server integration

You turn raw session metadata into a searchable database. The tool pulls `list_recordings` and embeds the browser type, duration, and exit page into your vector store. When a product manager asks for examples of mobile Safari users failing to submit a form, your RAG app finds the exact `get_recording` matches instantly.

Ground answers in real metrics

Hallucinations happen when agents lack hard data. By giving your FunctionAgent access to `list_funnels` and `get_survey_stats`, it grounds its answers in actual conversion math. You configure the McpToolSpec with include_resources=True. This lets the agent pull historical feedback widgets via `get_feedback_widget` to compare past and present user sentiment.

Setup guide

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

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Common questions about Hotjar MCP in LlamaIndex

Initialize BasicMCPClient with your Vinkius URL. Then wrap it in McpToolSpec and call await mcp_tool_spec.to_tool_list_async().

Yes. You pass an allowed_tools filter when setting up the spec. This prevents the agent from running `list_sites` if it only needs survey data.

It converts the structured JSON from `get_heatmap` into text nodes. You then embed these nodes to search for specific click patterns later.

Dashboards require manual filtering. A RAG pipeline lets you ask plain English questions and get answers backed by `list_feedback_responses` data.

The connection uses a single MCP endpoint token managed by Vinkius. When you pull `get_recording` metadata, the ephemeral sandbox processes the request and instantly terminates, leaving zero residual footprint.

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