How to Use the Coolify MCP in AutoGen

Q: How do I register the Coolify MCP Server tools in AutoGen?

Import the autogen-extmcp package and configure the StreamableHttpServerParams with your Vinkius endpoint to pass the tools to your AssistantAgent.

Q: How do AutoGen agents manage Coolify databases?

Agents use listdatabases to locate connection strings and negotiate the setup of new backend datastores via getdatabase.

Run multi-agent debates in AutoGen to safely validate and deploy Coolify applications across Docker swarms.

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Connect Coolify MCP to AutoGen

Create your Vinkius account to connect Coolify to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Coolify with AutoGen

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Consensus-Driven Deployments via AutoGen Agents

`trigger_deployment` performs a git fetch, rebuilds Nixpacks images, and rolls out the updated Docker image. This MCP Server lets a deployment agent propose this action, while a QA agent verifies the target branch using `get_application` before allowing the build. Broken builds are prevented from reaching your production environment. The agents debate the readiness of the commit, checking automatic rollout toggles before executing the container update.

Collaborative Server Cluster Management

`list_servers` identifies the raw physical endpoints running Docker swarms that host subsequent applications. AutoGen agents collaborate to balance workloads across these nodes by querying `get_server` to check executing ports and SSH statuses. When a node shows high latency, a performance agent coordinates with a system agent to identify empty slots. They negotiate which server should host the next application container.

Autonomous Incident Mitigation and App Recovery

`stop_application` halts execution algorithms to suspend a mapped application immediately during an incident. If a security agent detects an intrusion, it debates with the operations agent to decide whether to stop or restart the application. They can choose to run `restart_application` to inject fresh `.env` variables or pull the plug entirely. The final decision is reached through multi-agent consensus, minimizing downtime while securing the perimeter.

Setup guide

Set up Coolify MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Coolify tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Coolify_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Coolify data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Coolify_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Coolify data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Coolify. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Coolify MCP in AutoGen

Import the autogen-ext[mcp] package and configure the StreamableHttpServerParams with your Vinkius endpoint to pass the tools to your AssistantAgent.

Yes, a developer agent can propose a deploy, while a validator agent checks get_application to ensure the branch configuration is correct.

Agents use list_databases to locate connection strings and negotiate the setup of new backend datastores via get_database.

AutoGen supports both stdio and Streamable HTTP transports, which allows you to connect to hosted MCP servers over secure connections.

Vinkius manages your API keys securely, executing the MCP Server in an ephemeral, zero-trust sandbox that handles authentication directly without exposing raw credentials to the agents.

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