How to Use the Hotjar MCP in LangChain

Q: Can LangChain agents analyze Hotjar heatmaps?

Yes. The agent pulls the data array using getheatmap. It reads the click coordinates and scroll depth metrics to formulate a UX hypothesis.

Give your LangChain agents direct access to Hotjar session data to build automated UX analysis pipelines via this MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Hotjar MCP to LangChain

Create your Vinkius account to connect Hotjar to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Hotjar with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

LangChain ReAct agents for UX

Connect your agent to the `list_funnels` tool to identify exactly where users drop off. The agent grabs the highest-exit pages and automatically feeds those URLs into `list_heatmaps`. That's the advantage of a composable chain. Your script pulls the heatmap ID, runs `get_heatmap`, and summarizes the click distribution before a human ever looks at the dashboard.

Chain surveys to support tickets

Fetching raw data through `list_survey_responses` is just the start. You build a pipeline that reads low-score surveys, triggers `lookup_user` to find the customer, and formats a summary. Because you track this in LangSmith, you monitor exactly how many tokens the agent burned while processing `get_survey_stats`. The MCP Server handles the authentication, so your code stays clean.

Filter noise from session recordings

Nobody has time to watch hundreds of user sessions. Your script calls `list_recordings` to pull the day's traffic and filters out the bounced visits. Then it hits `get_recording` for the remaining sessions. The agent evaluates the metadata, flagging only the sessions where users repeatedly clicked unclickable elements.

Setup guide

Set up Hotjar MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Hotjar tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "hotjar-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Hotjar transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Hotjar. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Hotjar now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Hotjar MCP in LangChain

Use the MultiServerMCPClient with the Vinkius endpoint URL. You pass client.get_tools() directly into your create_agent setup.

Yes. The agent pulls the data array using `get_heatmap`. It reads the click coordinates and scroll depth metrics to formulate a UX hypothesis.

The server itself is stateless and ephemeral. You maintain conversation history using client.session() in your LangChain script.

LangSmith traces every interaction. You will see the exact inputs sent to `get_survey` and the JSON payload returned by the MCP endpoint.

Vinkius executes within a V8 Isolate Sandbox. When your code runs `lookup_user` or pulls session transcripts from `get_recording`, the data flows through a zero-trust tunnel and never touches a physical disk.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript