Coolify▌
by stumason
Manage servers and databases with Coolify—seamless system management server and SQL server tools via natural language.
Integrates with Coolify to enable natural language management of servers, projects, applications, and databases through the Coolify API, allowing users to perform DevOps operations without leaving their conversation interface.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / DevOps teams managing self-hosted infrastructure
- / Developers automating Coolify deployments
- / Teams coordinating multi-environment projects
capabilities
- / Create and manage servers with validation
- / Deploy and control application lifecycles
- / Track deployments and monitor services
- / Manage teams and project environments
- / Execute commands on running applications
- / Handle private key management
what it does
Manages Coolify infrastructure by creating, deploying, and monitoring servers, applications, and team resources through the Coolify API. Provides full lifecycle management of your self-hosted deployment platform.
about
Coolify is a community-built MCP server published by stumason that provides AI assistants with tools and capabilities via the Model Context Protocol. Manage servers and databases with Coolify—seamless system management server and SQL server tools via natural language. It is categorized under cloud infrastructure, productivity.
how to install
You can install Coolify in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Coolify is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Coolify MCP Server
The most comprehensive MCP server for Coolify - 38 optimized tools, smart diagnostics, documentation search, and batch operations for managing your self-hosted PaaS through AI assistants.
A Model Context Protocol (MCP) server for Coolify, enabling AI assistants to manage and debug your Coolify instances through natural language.
Features
This MCP server provides 38 token-optimized tools for debugging, management, and deployment:
| Category | Tools |
|---|---|
| Infrastructure | get_infrastructure_overview, get_mcp_version, get_version |
| Diagnostics | diagnose_app, diagnose_server, find_issues |
| Batch Operations | restart_project_apps, bulk_env_update, stop_all_apps, redeploy_project |
| Servers | list_servers, get_server, validate_server, server_resources, server_domains |
| Projects | projects (list, get, create, update, delete via action param) |
| Environments | environments (list, get, create, delete via action param) |
| Applications | list_applications, get_application, application (CRUD), application_logs |
| Databases | list_databases, get_database, database (create 8 types, delete), database_backups (CRUD schedules, view executions) |
| Services | list_services, get_service, service (create, update, delete) |
| Control | control (start/stop/restart for apps, databases, services) |
| Env Vars | env_vars (CRUD for application and service env vars) |
| Deployments | list_deployments, deploy, deployment (get, cancel, list_for_app) |
| Private Keys | private_keys (list, get, create, update, delete via action param) |
| GitHub Apps | github_apps (list, get, create, update, delete via action param) |
| Teams | teams (list, get, get_members, get_current, get_current_members) |
| Cloud Tokens | cloud_tokens (Hetzner/DigitalOcean: list, get, create, update, delete, validate) |
| Documentation | search_docs (full-text search across Coolify docs) |
Token-Optimized Design
The server uses 85% fewer tokens than a naive implementation (6,600 vs 43,000) by consolidating related operations into single tools with action parameters. This prevents context window exhaustion in AI assistants.
Installation
Prerequisites
- Node.js >= 18
- A running Coolify instance (tested with v4.0.0-beta.460)
- Coolify API access token (generate in Coolify Settings > API)
Claude Desktop
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"coolify": {
"command": "npx",
"args": ["-y", "@masonator/coolify-mcp"],
"env": {
"COOLIFY_ACCESS_TOKEN": "your-api-token",
"COOLIFY_BASE_URL": "https://your-coolify-instance.com"
}
}
}
}
Claude Code
claude mcp add coolify \
-e COOLIFY_BASE_URL="https://your-coolify-instance.com" \
-e COOLIFY_ACCESS_TOKEN="your-api-token" \
-- npx @masonator/coolify-mcp@latest
Note: Use
@latesttag (not-yflag) for reliable startup in Claude Code CLI.
Cursor
env COOLIFY_ACCESS_TOKEN=your-api-token COOLIFY_BASE_URL=https://your-coolify-instance.com npx -y @masonator/coolify-mcp
Context-Optimized Responses
Why This Matters
The Coolify API returns extremely verbose responses - a single application can contain 91 fields including embedded 3KB server objects and 47KB docker-compose files. When listing 20+ applications, responses can exceed 200KB, which quickly exhausts the context window of AI assistants like Claude Desktop.
This MCP server solves this by returning optimized summaries by default.
How It Works
| Tool Type | Returns | Use Case |
|---|---|---|
list_* | Summaries only (uuid, name, status, etc) | Discovery, finding resources |
get_* | Full details for a single resource | Deep inspection, debugging |
get_infrastructure_overview | All resources summarized in one call | Start here to understand your setup |
Response Size Comparison
| Endpoint | Full Response | Summary Response | Reduction |
|---|---|---|---|
| list_applications | ~170KB | ~4.4KB | 97% |
| list_services | ~367KB | ~1.2KB | 99% |
| list_servers | ~4KB | ~0.4KB | 90% |
| list_application_envs | ~3KB/var | ~0.1KB/var | 97% |
| deployment get | ~13KB | ~1KB | 92% |
HATEOAS-style Response Actions
Responses include contextual _actions suggesting relevant next steps:
{
"data": { "uuid": "abc123", "status": "running" },
"_actions": [
{ "tool": "application_logs", "args": { "uuid": "abc123" }, "hint": "View logs" },
{
"tool": "control",
"args": { "resource": "application", "action": "restart", "uuid": "abc123" },
"hint": "Restart"
}
],
"_pagination": { "next": { "tool": "list_applications", "args": { "page": 2 } } }
}
This helps AI assistants understand logical next steps without consuming extra tokens.
Recommended Workflow
- Start with overview:
get_infrastructure_overview- see everything at once - Find your target:
list_applications- get UUIDs of what you need - Dive deep:
get_application(uuid)- full details for one resource - Take action:
control(resource: 'application', action: 'restart'),application_logs(uuid), etc.
Pagination
All list endpoints still support optional pagination for very large deployments:
# Get page 2 with 10 items per page
list_applications(page=2, per_page=10)
Example Prompts
Getting Started
Give me an overview of my infrastructure
Show me all my applications
What's running on my servers?
Debugging & Monitoring
Diagnose my stuartmason.co.uk app
What's wrong with my-api application?
Check the status of server 192.168.1.100
Find any issues in my infrastructure
Get the logs for application {uuid}
What environment variables are set for application {uuid}?
Show me recent deployments for application {uuid}
What resources are running on server {uuid}?
Application Management
Restart application {uuid}
Stop the database {uuid}
Start service {uuid}
Deploy application {uuid} with force rebuild
Update the DATABASE_URL env var for application {uuid}
Project Setup
Create a new project called "my-app"
Create a staging environment in project {uuid}
Deploy my app from private GitHub repo org/repo on branch main
Deploy nginx:latest from Docker Hub
Deploy from public repo https://github.com/org/repo
Documentation & Help
How do I set up Docker Compose with Coolify?
Search the docs for health check configuration
How do I fix a 502 Bad Gateway error?
What are Coolify environment variables?
Teams & Cloud Providers
Who has access to my Coolify instance?
Show me the current team members
List my cloud provider tokens
Validate my Hetzner API token
Environment Variables
| Variable | Required | Default | Description |
|---|---|---|---|
COOLIFY_ACCESS_TOKEN | Yes | - |
FAQ
- What is the Coolify MCP server?
- Coolify is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Coolify?
- This profile displays 46 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★46 reviews- ★★★★★Ganesh Mohane· Dec 24, 2024
We wired Coolify into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Carlos Robinson· Dec 24, 2024
Strong directory entry: Coolify surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Diya Bansal· Dec 24, 2024
I recommend Coolify for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Maya Martin· Dec 20, 2024
Coolify is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Sakura Verma· Dec 4, 2024
Useful MCP listing: Coolify is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Carlos Park· Nov 27, 2024
Coolify has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Evelyn Flores· Nov 23, 2024
Coolify reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Sakshi Patil· Nov 15, 2024
Strong directory entry: Coolify surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Maya Dixit· Nov 15, 2024
We wired Coolify into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Carlos Tandon· Nov 15, 2024
According to our notes, Coolify benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
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