Can my agent trigger a new deployment for a Coolify application?

Yes. Use the 'trigger_deployment' tool with the application UUID. The agent will initiate a full build pipeline, fetching the latest commit and rolling out the updated version directly on your server.

How do I retrieve the internal connection string for a managed database?

Provide the database UUID to the 'get_database' tool. Your agent will extrapolate the internal network URI (e.g., for PostgreSQL or MySQL), allowing you to securely link your apps without external port exposure.

Can I restart an application to apply new environment variables?

Absolutely. Use the 'restart_application' tool. The agent will execute an overlapping bounce, destroying old instances and initializing fresh replicas with the latest configuration and environment variables.

Coolify MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Coolify
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Coolify through Vinkius, pass the Edge URL in the `mcps` parameter and every Coolify tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Coolify Specialist",
    goal="Help users interact with Coolify effectively",
    backstory=(
        "You are an expert at leveraging Coolify tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Coolify "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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High SecurityEnterprise-grade

IAMAccess control

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DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Coolify MCP Server

Connect your Coolify instance to any AI agent and take full control of your self-hosting and private cloud workflows through natural conversation.

When paired with CrewAI, Coolify becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Coolify tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Server Monitoring — List self-hosted nodes and retrieve intricate networking parameters including IP properties and Docker swarm statuses
Application Management — List all managed frontend/backend apps and fetch elaborate internal topology metrics like mapped GitHub branches and Traefik proxy paths
Lifecycle Control — Start, stop, and restart applications natively, allowing you to recycle container states and apply configuration updates instantly
Deployment Automation — Trigger raw build pipelines to fetch the latest commits, rebuild Nixpacks images, and roll out updated Docker versions
Database Oversight — Manage PostgreSQL, MySQL, and Redis configurations and extrapolate internal connection strings for secure application linking
Resource Navigation — asociating Project repositories to explicit application UUIDs required for downstream mutations and operational auditing

The Coolify MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Coolify to CrewAI via MCP

Follow these steps to integrate the Coolify MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Coolify

Why Use CrewAI with the Coolify MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Coolify through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Coolify + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Coolify MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Coolify for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Coolify, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Coolify tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Coolify against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Coolify MCP Tools for CrewAI (10)

These 10 tools become available when you connect Coolify to CrewAI via MCP:

get_application

Examines mapped GitHub branches, automatic rollout toggles (push to deploy), and assigned Traefik reverse proxy FQDN paths. Fetch elaborate internal topology metrics for a given Application

get_database

Highly required when linking newly provisioned Web Apps to Backend Datastores. Extrapolate internal configuration arrays for a Database

get_server

Verifies IP properties, SSH connection validation statuses, and Docker executing ports resolving across the cluster. Get configuration schema mapped to a specific Coolify Server Node

list_applications

Generates the crucial map associating Project repositories to explicit application UUIDs required for downstream mutations (like restarting and stopping). List all frontend/backend Applications actively managed by Coolify

list_databases

Isolates database bounding boxes mapping to applications so you can properly retrieve Connection Strings and backup cadence timelines. List managed PostgreSQL, MySQL, and Redis configurations

list_servers

Used to identify the raw physical endpoints running Docker swarms that host subsequent applications. List all self-hosted Server Nodes attached to Coolify

restart_application

Ensures updated config `.env` variables injected via Coolify take effect immediately in runtime RAM. Bounce a Coolify application recycling its container states

start_application

Spin up containers mapped to a suspended Application UUID

stop_application

Used precisely for pausing billing or restricting web perimeter ingress during a cyber incident directly via the Coolify dashboard API. Halt execution algorithms suspending the mapped Application

trigger_deployment

Performs `git fetch`, rebuilds Nixpacks images, caches dependencies, and rolls the updated Docker image out directly over the previous active application version. Trigger a raw build pipeline fetching the latest Git commit

Example Prompts for Coolify in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Coolify immediately.

"List all active servers in my Coolify instance"

"Trigger a deployment for application 'backend-api'"

"What is the connection string for database 'user-db-prod'?"

Troubleshooting Coolify MCP Server with CrewAI

Common issues when connecting Coolify to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Coolify + CrewAI FAQ

Common questions about integrating Coolify MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.