How to Use the Hotjar MCP in Google ADK

Q: How do I register the Hotjar toolset inside my Google ADK agent?

Instantiate McpToolset using your Vinkius HTTP endpoint and pass it to your LlmAgent tools array. The framework automatically maps all fifteen API tools, making them instantly available to Gemini models.

Q: Can a Google ADK agent export Hotjar heatmap coordinates to BigQuery?

Yes. The agent calls getheatmap to retrieve the coordinate maps and can format that raw JSON payload for direct ingestion into BigQuery tables, allowing you to run SQL queries over visual click maps.

Q: How do I limit which Hotjar tools my Gemini agent can access via MCP?

Use the optional toolnames filter when initializing your McpToolset. This lets you restrict your agent to harmless read-only tools like listsurveys while blocking access to user lookup features.

Q: How does this MCP integration handle GDPR compliance for survey responses?

When pulling data via listsurveyresponses, the server respects the anonymization settings configured in your main dashboard. Your Google ADK environment receives pre-scrubbed text, keeping raw personally identifiable information completely out of your LLM training loops.

Feed raw user feedback and heatmaps directly to your Google ADK enterprise agents for deep cohort analysis.

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Connect Hotjar MCP to Google ADK

Create your Vinkius account to connect Hotjar to Google ADK 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 Google ADK

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Correlate BigQuery Cohorts with Google ADK

`list_recordings` retrieves specific session replays for user segments you have already flagged in your database. This MCP Server tool connects your data warehouse cohorts to actual on-screen behavior. Your agent matches SQL transaction anomalies with visual session replays. By feeding these recordings directly into Gemini's massive context window, you can analyze hours of user struggle in a single execution pass.

Categorize Surveys with Google ADK

`list_surveys` identifies active feedback channels across all your tracked web properties. The agent uses this tool to pull raw text responses via `list_survey_responses` for immediate sentiment classification. Gemini processes these unstructured responses inside your enterprise pipeline, matching qualitative complaints against known system bugs. This lets your support team prioritize fixes based on the financial value of the affected users.

Monitor Multiple Tracking Sites

`list_sites` returns every web property currently configured under your organization's tracking plan. The agent calls this tool to verify that tracking scripts are active and collecting data correctly. Combined with `check_hotjar_status`, this setup automates routine telemetry checks. Your agent flags silent tracking failures before they corrupt your product metrics, keeping your data pipeline clean and reliable.

Setup guide

Set up Hotjar MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Hotjar tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Hotjar_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Hotjar tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Hotjar MCP in Google ADK

Instantiate `McpToolset` using your Vinkius HTTP endpoint and pass it to your `LlmAgent` tools array. The framework automatically maps all fifteen API tools, making them instantly available to Gemini models.

Yes. The agent calls `get_heatmap` to retrieve the coordinate maps and can format that raw JSON payload for direct ingestion into BigQuery tables, allowing you to run SQL queries over visual click maps.

Yes, the server runs on Vinkius using secure, streamable HTTP transport. You can configure your Google ADK agent to communicate with it over standard web protocols, ensuring compatibility with your cloud VPC rules.

Use the optional `tool_names` filter when initializing your `McpToolset`. This lets you restrict your agent to harmless read-only tools like `list_surveys` while blocking access to user lookup features.

When pulling data via `list_survey_responses`, the server respects the anonymization settings configured in your main dashboard. Your Google ADK environment receives pre-scrubbed text, keeping raw personally identifiable information completely out of your LLM training loops.

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