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 Pydantic AI 10 tools — connect in under 2 minutes

Name: Coolify
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Coolify through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Coolify "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Coolify?"
    )
    print(result.data)

asyncio.run(main())

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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.

Pydantic AI validates every Coolify tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Coolify MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Coolify with type-safe schemas

Why Use Pydantic AI with the Coolify MCP Server

Pydantic AI provides unique advantages when paired with Coolify through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Coolify integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Coolify connection logic from agent behavior for testable, maintainable code

Coolify + Pydantic AI Use Cases

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

Type-safe data pipelines: query Coolify with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Coolify tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Coolify and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Coolify responses and write comprehensive agent tests

Coolify MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Coolify to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Coolify + Pydantic AI FAQ

Common questions about integrating Coolify MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Coolify MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.