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Conversational Kubernetes management with GPT-4o and MCP integration

Reza GholizadeReza Gholizade
3727 views
2/3/2026
AirtableScheduledTypeformAutomationData Sync
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This n8n workflow template uses community nodes and is only compatible with the self-hosted version of n8n.

Conversational Kubernetes Management with GPT-4o and MCP Integration

This workflow enables you to manage Kubernetes clusters conversationally using OpenAI’s GPT-4o and a secure MCP (Model Context Protocol) server. It transforms natural language queries into actionable Kubernetes commands via a lightweight MCP API gateway — perfect for developers and platform engineers seeking to simplify cluster interaction.

🚀 Setup Instructions

  1. Import the Workflow
    Upload this template to your n8n instance.

  2. Configure Required Credentials

    • OpenAI API Key: Add your GPT-4o API key in the credentials.
    • MCP Client Node: Set the URL and auth for your MCP server.

  3. Test Kubernetes Access
    Ensure your MCP server is correctly configured and has access to the target Kubernetes cluster.

🧩 Prerequisites

  • n8n version 0.240.0 or later
  • Access to GPT-4o via OpenAI
  • A running MCP server
  • Kubernetes cluster credentials configured in your MCP backend

⚠️ Community Nodes Disclaimer

This workflow uses custom community nodes (e.g., MCP Client).
Make sure to review and trust these nodes before running in production.

🛠️ How It Works

  • A webhook or chat input triggers the conversation.
  • GPT-4o interprets the message and generates structured Kubernetes queries.
  • MCP Client securely sends requests to your cluster.
  • The result is returned and formatted for easy reading.

🔧 Customization Tips

  • Tweak the GPT-4o prompt to match your tone or technical level.
  • Extend MCP endpoints to support new Kubernetes actions.
  • Add alerting or monitoring integrations (e.g., Slack, Prometheus).

🖼️ Template Screenshot

Screenshot 14040428 at 12.42.32.png

🧠 Example Prompts

Show me all pods in the default namespace.
Get logs for nginx pod in kube-system.
List all deployments in staging.

📎 Additional Resources

Build smarter Kubernetes workflows with the power of AI !

Conversational AI Agent for Model Context Protocol (MCP)

This n8n workflow sets up a conversational AI agent that can interact with and respond to messages received via a chat trigger or an MCP server trigger. It leverages OpenAI's chat models for natural language understanding and generation, incorporating memory to maintain context in conversations. Additionally, it integrates with an MCP Client Tool, suggesting its capability to interact with other Model Context Protocol services or agents.

What it does

This workflow automates the following steps:

  1. Listens for Chat Messages or MCP Requests: It can be triggered by either a direct chat message (e.g., from a connected chat service) or an incoming request from an MCP (Model Context Protocol) server.
  2. Initializes Conversational AI Agent: Upon receiving a message, it activates an AI Agent designed for conversational interactions.
  3. Utilizes OpenAI Chat Model: The AI Agent employs an OpenAI Chat Model (like GPT-4o) to process the incoming message, understand its intent, and formulate a response.
  4. Maintains Conversation Context: A "Simple Memory" component is used to store and recall previous turns in the conversation, enabling the AI to maintain context and provide more coherent and relevant responses.
  5. Interacts with MCP Client Tool: The AI Agent is equipped with an "MCP Client Tool," indicating its ability to send requests or interact with other services or agents that adhere to the Model Context Protocol, potentially for executing specific actions or retrieving information.
  6. Responds to the User: The AI Agent generates a response based on its understanding, memory, and potential MCP interactions, which would then be sent back to the original chat or MCP trigger.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • OpenAI API Key: An API key for OpenAI to use their chat models. This will need to be configured as a credential in n8n for the "OpenAI Chat Model" node.
  • Model Context Protocol (MCP) compatible services: If you intend to use the "MCP Client Tool", you will need access to or an understanding of MCP-compatible services or endpoints that the agent can interact with.
  • Chat Service Integration (Optional): If you want to use the "When chat message received" trigger, you will need to configure a chat service integration (e.g., Slack, Telegram, Discord, etc.) with n8n.

Setup/Usage

  1. Import the workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • For the "OpenAI Chat Model" node, set up an OpenAI API Key credential.
  3. Configure Triggers:
    • Chat Trigger: If using the "When chat message received" trigger, configure it for your desired chat service.
    • MCP Server Trigger: If using the "MCP Server Trigger," ensure it's configured to listen on the appropriate endpoint and port for incoming MCP requests.
  4. Activate the Workflow: Once configured, activate the workflow.
  5. Start Conversing:
    • Send a message to your configured chat service to interact with the AI agent.
    • Send an MCP request to the "MCP Server Trigger" endpoint to engage the AI agent via the Model Context Protocol.

The AI agent will process your input, use its memory to maintain context, potentially interact with MCP services, and then respond.

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