by stumason
Manage servers and databases with Coolify—seamless system management server and SQL server tools via natural language.
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.
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.
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.
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.
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
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
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
Share your MCP server with the developer community
We wired Coolify into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: Coolify surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend Coolify for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Coolify is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Coolify is the kind of server we cite when onboarding engineers to host + tool permissions.
Coolify has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Coolify reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Strong directory entry: Coolify surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We wired Coolify into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, Coolify benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
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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.
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) |
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.
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 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.
env COOLIFY_ACCESS_TOKEN=your-api-token COOLIFY_BASE_URL=https://your-coolify-instance.com npx -y @masonator/coolify-mcp
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.
| 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 |
| 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% |
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.
get_infrastructure_overview - see everything at oncelist_applications - get UUIDs of what you needget_application(uuid) - full details for one resourcecontrol(resource: 'application', action: 'restart'), application_logs(uuid), etc.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)
Give me an overview of my infrastructure
Show me all my applications
What's running on my servers?
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}?
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}
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
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?
Who has access to my Coolify instance?
Show me the current team members
List my cloud provider tokens
Validate my Hetzner API token
| Variable | Required | Default | Description |
|---|---|---|---|
COOLIFY_ACCESS_TOKEN | Yes | - |
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ 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.