Connect
Optimize
Secure
Live 4 Actions
Hotjar MCP Server
for AI Agents
Connect your AI agent to StackOne's Hotjar MCP server and give it ready-to-use MCP tools out of the box. Auth, tool execution, and security all managed.
Hotjar MCP Server
Built by 
StackOne
StackOne 
Coverage
4 Agent Actions
Create, read, update, and delete across Hotjar — and extend your agent's capabilities with custom actions.
Authentication
Agent Tool Authentication
Per-user OAuth in one call. Your Hotjar MCP server gets session-scoped tokens with zero credentials stored on your infra.
Agent Auth →
Security
Agent Protection
Every Hotjar tool response scanned for prompt injection in milliseconds — 88.7% accuracy, all running on CPU.
Prompt Injection Defense →
Performance
Max Agent Context. Min Cost.
Free up to 96% of your agent's context window to enhance reasoning and reduce cost, on every Hotjar call.
Tools Discovery →What is the Hotjar MCP Server?
A Hotjar MCP server lets AI agents read and write Hotjar data through the Model Context Protocol — Anthropic's open standard for connecting LLMs to external tools. StackOne's Hotjar MCP server ships with pre-built actions, fully extensible via the Connector Builder — plus managed authentication, prompt injection defense, observability, and agent execution runtime. Connect it from MCP clients like Claude Desktop, Claude Code, Cursor, Goose, and VS Code, or from agent frameworks like OpenAI Agents SDK, LangChain, and Vercel AI SDK.
All Hotjar MCP Tools
Every action from Hotjar's API, ready for your agent. Create, read, update, and delete — scoped to exactly what you need.
Surveys
- List Surveys
List all surveys configured on a Hotjar site with cursor-based pagination
- Get Survey
Retrieve full details of a single survey including its question definitions
Survey Responses
- List Survey Responses
List collected responses for a specific survey with cursor-based pagination
Look Up User Datas
- Look Up User Data
Asynchronously look up (and optionally delete) all Hotjar data associated with a specific end-user across all sites in the organization
Hotjar AI Agent Use Cases
Connect your AI agent to Hotjar and help your team scale the support operations they run by hand today.
Ticket Triage
Use StackOne to connect your AI agent to your helpdesk, CRM, and messaging tools to automate ticket triage and priority routing.
View Voice Call Summarization
Use StackOne to connect your AI agent to your telephony, CRM, and messaging tools to automate voice call summarization and CRM logging.
View SLA Breach Prediction
Use StackOne to connect your AI agent to your helpdesk, CRM, and messaging tools to automate SLA breach prediction and escalation.
ViewSet Up Your Hotjar MCP Server in Minutes
One endpoint. Any framework. Your agent is talking to Hotjar in under 10 lines of code.
Agent Frameworks
JSON{
"mcpServers": {
"stackone": {
"command": "npx",
"args": [
"-y",
"mcp-remote@latest",
"https://api.stackone.com/mcp?x-account-id=<account_id>",
"--header",
"Authorization: Basic <YOUR_BASE64_TOKEN>"
]
}
}
}Check More Data & Analytics MCP Servers
Smartsheet
189+ actions
Tableau
122+ actions
SurveyMonkey
104+ actions
Microsoft Excel
102+ actions
Qlik
101+ actions
Snowflake
80+ actions
ZoomInfo
73+ actions
Platform Resources
MCP Code Mode: Keeping Tool Responses Out of Agent Context
Anthropic's code_execution processes data already in context. Custom MCP code mode keeps raw tool responses in a sandbox. 14K tokens vs 500.
11 min
Comparing BM25, TF-IDF, and Hybrid Search for MCP Tool Discovery
Benchmarking BM25, TF-IDF, and hybrid search for MCP tool discovery across 916 tools. The 80/20 TF-IDF/BM25 hybrid hits 21% Top-1 accuracy in under 1ms.
10 min
Indirect Prompt Injection Defense for MCP Tools: A Technical Guide
MCP tools that read emails, CRM records, and tickets are indirect prompt injection vectors. Here's how we built a two-tier defense that scans tool results in ~11ms.
12 min
MCP vs A2A: Architecture, Security, and When to Use Each
MCP vs A2A: what each protocol standardizes, how they differ, their shared security risks including indirect prompt injection, and when to use one, both, or a hybrid architecture.
12 min
MCP vs API: What 200+ Connector Builds Taught Us
MCP wraps APIs, it doesn't replace them. After building 200+ connectors that serve both, here's when each approach wins.
14 min read
Hotjar MCP Server FAQ
Does StackOne have a Hotjar MCP server?
Yes. StackOne offers a hosted Hotjar MCP server, and every action is tested and QA'd by StackOne. Connect it to Claude, Cursor, and any other MCP client, or to any agent framework through the AI Action SDK. It ships with managed agent authentication, prompt injection defense, and tool discovery with server-side execution that preserve your agent's context window and keep reasoning performance.
Hotjar MCP server vs direct API integration — what's the difference?
A Hotjar MCP server and direct API integration serve different use cases. Direct API integration is for software-to-software — backend code calling Hotjar. A Hotjar MCP server is for AI agents — MCP clients like Claude and Cursor, plus framework agents built with OpenAI, LangChain, or Vercel AI — discovering and calling Hotjar at runtime. StackOne provides both.
How does Hotjar authentication work for AI agents?
Hotjar authentication for AI agents works through a StackOne Connect Session. Create one via the dashboard or the SDK — you get an auth link and ready-to-paste config for Claude Desktop, Cursor, and other MCP clients. Your user authenticates their own Hotjar account; StackOne handles token exchange, storage, and refresh. Credentials never reach the LLM, and each user is isolated via
origin_owner_id.Are Hotjar MCP tools vulnerable to prompt injection?
Yes — Hotjar MCP tools can be vulnerable to indirect prompt injection. Any tool that reads user-written content — documents, messages, tickets, records, or free-text fields — is a potential vector. StackOne Defender scans every tool response before it enters the agent's context — regex patterns in ~1ms, then a MiniLM classifier in ~4ms. 88.7% accuracy, CPU-only.
What is the context bloat of a Hotjar agent and how do I avoid it?
Context bloat happens when Hotjar tool schemas and API responses eat your Hotjar agent's memory, preventing it from reasoning effectively. A single Hotjar query can return a massive JSON response, and connecting multiple tools compounds the problem. Tools Discovery and Code Mode reduce context bloat — loading only relevant tools per query and keeping raw responses out of the agent's context.
Can I limit which actions my Hotjar agent can access?
Yes — you can limit which actions your Hotjar agent can access directly from the StackOne dashboard. Toggle actions on or off, or restrict them to specific accounts, with no code changes to your agent. Session tokens can be scoped to exact actions so if one leaks, exposure stays contained.
Can I create custom agent actions for my Hotjar MCP server?
Yes — you can create custom agent actions for your Hotjar MCP server using Connector Builder. It's an integration agent your coding assistant (Claude Code, Cursor, or Copilot) can invoke to research Hotjar's API, generate production-ready connector YAML, test against the live API, and validate before you ship.
When should I NOT use a Hotjar MCP server?
Skip a Hotjar MCP server if your integration is purely software-to-software — direct Hotjar API integration is simpler when no AI agent is involved. For deterministic, compliance-critical operations (financial transactions, regulatory reporting), direct API gives you predictable behavior without agent-driven decision-making. MCP shines when AI agents need to dynamically discover and call Hotjar actions at runtime.
What AI frameworks and AI clients does the StackOne Hotjar MCP server support?
The StackOne Hotjar MCP server supports both. MCP clients (paste-and-go apps): Claude Desktop, Claude Code, Cursor, VS Code, Goose. Agent frameworks (code SDKs you build with): OpenAI Agents SDK, Anthropic, Vercel AI, Google ADK, CrewAI, Pydantic AI, LangChain, LangGraph, Azure AI Foundry.
Put your AI agents to work
All the tools you need to build and scale AI agent integrations, with best-in-class connectivity, execution, and security.