Coolify MCP Server: Give AI Agents Access to Your Deployments, Services, and Server Resources

Why Give AI Access to Coolify?

Coolify is the open-source, self-hosted alternative to Heroku, Netlify, and Vercel. It handles Git-based deployments, Docker services, databases, and server management from a single dashboard. Teams use it to deploy applications without vendor lock-in.

But when you're debugging in Claude or Cursor, your AI assistant can't see your Coolify dashboard. It can't check if a deployment succeeded, read build logs, or tell you whether your database is running. You end up copying error messages and deployment URLs back and forth.

With DataFaucet, you browse your Coolify dashboard once and it captures the API calls as MCP tools. Now your AI coding assistant can query deployment state, read logs, and check server health directly.

What AI Agents Can Do With Coolify Access

Once DataFaucet creates an MCP server from your Coolify session, AI agents can:

• Check deployment status for all applications, showing which are running, building, or failed
• Read build logs to diagnose why a deployment failed without opening the browser
• Monitor server resources (CPU, RAM, disk) across all connected servers
• List services and databases with their current state and connection details
• View environment variables to verify configuration is correct
• Check domain and SSL status for deployed applications
• See webhook and Git integration state to diagnose CI/CD issues

Setting Up Coolify as an MCP Server with DataFaucet

1. Open your Coolify dashboard and navigate through deployments, services, and server pages
2. DataFaucet captures the API calls your browser makes to Coolify's backend
3. Click "Create MCP Server" and DataFaucet generates structured tools from those endpoints
4. Add the MCP server URL to your AI client (Claude, Cursor, Windsurf)
5. Your AI can now query Coolify data in real-time

The entire process takes about two minutes. Works with any self-hosted Coolify instance.

Example Queries Your AI Can Answer

After connecting, try asking your AI:

• "Did my latest push to main deploy successfully?"
• "What's using the most memory on the production server?"
• "Show me the build logs for the failed API deployment"
• "Which databases are running and what are their connection strings?"
• "Is the staging environment healthy?"
• "What version is currently deployed for the frontend app?"

Your AI can answer these instantly because it has real-time access to your Coolify API through MCP tools.

DataFaucet vs Coolify's REST API

Self-Hosted Coolify Works Perfectly

Since DataFaucet captures API calls from your browser, it works with any Coolify instance regardless of where it runs. Your AI assistant gets access to the same data you see in the dashboard, whether Coolify is on a VPS, bare metal, or your homelab.

Use Cases

• Deployment debugging: Ask "why did the deploy fail?" and get build logs instantly
• Resource awareness: AI flags servers approaching capacity while you code
• Multi-app management: Quick status checks across dozens of deployed services
• Database monitoring: Verify databases are running and check connection health
• Team onboarding: New developers can ask AI about deployment state without learning the Coolify UI

Get Started

DataFaucet turns your Coolify dashboard into structured MCP tools in under two minutes. No custom API code, no token management, no webhook configuration.

Create your Coolify MCP server and give your AI coding assistant full visibility into your self-hosted deployments.