How to Monitor Website Uptime with AI Using MCP and Claude
AI assistants are getting better at writing code, answering questions, and analyzing data. But until recently, they couldn't interact with your actual infrastructure. That's changing with the Model Context Protocol (MCP) — and uptime monitoring is one of the first areas where this matters.
In this guide, you'll learn how to connect Claude (or any MCP-compatible AI assistant) to UptyBots, so you can check uptime, review incidents, and manage monitors through natural language.
The Problem with Traditional Monitoring Dashboards
Monitoring dashboards are essential, but they have limitations:
- Context switching — you're working in your IDE or terminal, but need to open a browser tab to check if your deployment caused any issues
- Manual correlation — when something goes wrong, you flip between monitoring, logs, and chat to piece together what happened
- Repetitive checks — "Is everything green?" is a question you ask ten times a day but never automate
- Setup overhead — creating 20 monitors for a new project means 20 form submissions
What if your AI assistant could answer "is everything green?" instantly — with real data, not guesses?
What is MCP?
The Model Context Protocol is an open standard by Anthropic that gives AI assistants the ability to call external tools. When you add an MCP server to your AI client, the assistant gains new capabilities — like reading your monitoring data or creating monitors.
The key insight: the MCP server runs on your local machine. Your AI client starts it as a subprocess, and all communication happens over stdin/stdout. No cloud relay, no third-party access to your data.
Setting Up AI-Powered Monitoring
What You'll Need
- A UptyBots account (free signup)
- An API key from Account → API Keys
- Node.js 18 or later
- Claude Desktop, Claude Code, or Cursor
Step 1: Install the MCP Server
git clone https://github.com/oleprog-uptybots/mcp-server.git
cd mcp-server
npm install
Step 2: Add It to Your AI Client
For Claude Desktop, edit claude_desktop_config.json:
{
"mcpServers": {
"uptybots": {
"command": "node",
"args": ["/path/to/mcp-server/index.js"],
"env": {
"UPTYBOTS_API_URL": "https://uptybots.com",
"UPTYBOTS_API_KEY": "upty_your_key_here"
}
}
}
}
For Claude Code, add to ~/.claude/settings.json with the same structure. For Cursor, add to .cursor/mcp.json in your project.
Step 3: Start Talking to Your Monitors
Restart your AI client and try these prompts:
- "List all my monitors and tell me if anything is down"
- "Create an HTTP monitor for https://mysite.com"
- "Show me incidents for the last 7 days"
- "What's the average response time for my API monitor this week?"
What AI-Powered Monitoring Looks Like in Practice
Morning Standup Check
Instead of opening your dashboard every morning, ask your AI assistant: "Give me a quick health check of all my monitors." It calls list_monitors, checks which ones are up, down, or paused, and gives you a summary in seconds.
Incident Post-Mortem
After an outage, ask: "What happened with my production API between 2am and 5am yesterday?" The assistant pulls incident data, correlates timestamps, and presents a timeline — no dashboard clicking needed.
Bulk Monitor Setup
Launching a new project with 10 services? Tell your assistant: "Create HTTP monitors for these URLs: api.myapp.com, web.myapp.com, admin.myapp.com..." It creates each monitor and reports back.
Deployment Safety
Before a deploy: "Pause all monitors for staging.myapp.com." After deploy: "Resume them and tell me if anything is failing."
Traditional Dashboard vs AI-Assisted Monitoring
| Task | Dashboard | AI + MCP |
|---|---|---|
| Check if anything is down | Open browser, navigate, scan visually | "Any monitors down?" — instant answer |
| Review yesterday's incidents | Click through monitors, filter by date | "Show yesterday's incidents" — summarized |
| Create 5 new monitors | Fill form 5 times | One message with all URLs |
| Maintenance mode | Pause each monitor manually | "Pause all staging monitors" |
| Performance analysis | Export data, build charts | "What's the trend for response times this month?" |
The dashboard isn't going away — it's still the best tool for visual oversight and configuration. But for quick checks, bulk operations, and conversational analysis, AI + MCP is faster.
Security and Privacy
- The MCP server runs locally — no data leaves your machine except API calls to UptyBots
- Authentication uses API keys (SHA-256 hashed on our servers)
- Each key is scoped to your account — no cross-user data access
- Keys can be revoked instantly from your dashboard
Getting Started
The UptyBots MCP server is open-source and free to use with any UptyBots plan. Here's everything you need:
- MCP Server Documentation — full setup guide and tool reference
- REST API Reference — the API that powers the MCP server
- Create a free account — you'll need one to connect
AI-powered monitoring isn't a future concept — it works today. Set it up in five minutes and see the difference.