k8s-port-forward-mcp

Kubernetes Port Forward MCP

A Model Context Protocol (MCP) server that provides tools for discovering Kubernetes services and running kubectl port-forward sessions (optionally with separate log windows). It is designed for MCP clients/LLMs that translate natural language into structured tool calls.

Kubernetes Port Forward MCP vs kubectl port-forward

This package provides an MCP interface on top of the standard kubectl workflow.

• kubectl: Great when you already know the exact namespace/pod/ports. You run commands manually and manage multiple terminals yourself.
• MCP: Better when an agent needs to discover services and run multiple port-forwards in a single session via tools (plus optional log windows), while returning copy/paste-ready commands.

Key Features

• Service discovery: list namespaces and infer services (short name → environments → namespace) from running pods.
• Multi-service in one session: start multiple port-forwards with one tool call.
• LLM-friendly API: start_k8s_port_forward accepts an array so each service can use different namespace, environment, localPort, and remotePort.
• Optional logs: open kubectl logs -f in a separate OS-level terminal window per service.

Requirements

• Node.js 18 or newer
• kubectl installed and configured with access to your cluster
• VS Code, Cursor, Windsurf, Claude Desktop, Cline, or any other MCP client

Getting started

First, install the Kubernetes Port Forward MCP server with your client.

Standard config works in most MCP clients:

{
  "mcpServers": {
    "k8s-port-forward": {
      "command": "npx",
      "args": ["-y", "k8s-port-forward-mcp@latest"]
    }
  }
}

Follow the instruction in the section Configuring MCP Servers

Example: add to your cline_mcp_settings.json:

{
  "mcpServers": {
    "k8s-port-forward": {
      "type": "stdio",
      "command": "npx",
      "timeout": 30,
      "args": ["-y", "k8s-port-forward-mcp@latest"],
      "disabled": false
    }
  }
}

claude mcp add k8s-port-forward npx -y k8s-port-forward-mcp@latest

Follow the MCP install guide and use the standard config above.

Go to Cursor Settings → MCP → Add new MCP Server and paste the standard config above.

Configuration

This server currently does not expose additional CLI flags beyond the MCP stdio transport. Most configuration happens through tool arguments:

• start_k8s_port_forward.services[].namespace — pick a specific namespace
• start_k8s_port_forward.services[].environment — one of dev, qa, stg, prod (used to resolve the right pod)
• start_k8s_port_forward.services[].remotePort — set a remote port explicitly (otherwise detected from the Service; defaults to 3000 if detection fails)
• start_k8s_port_forward.services[].includeLogs — open a separate log window (default: true)

Output and logs

• The MCP server runs as a separate process and cannot create VS Code integrated terminals.
• Port-forwards run in the MCP server process; output is prefixed per service.
• When includeLogs is enabled, logs open in a separate OS-level terminal window.
• The tool response includes the exact kubectl commands, so you can copy/paste them into your own terminals if preferred.

Validation and Debugging

Since the MCP server spawns actual kubectl processes in the background, you may want to verify what's running and see the exact commands being executed.

Checking running kubectl processes

Windows (PowerShell):

# List all kubectl processes
tasklist /fi "imagename eq kubectl.exe"

# See the exact commands with process IDs
Get-CimInstance Win32_Process -Filter "Name='kubectl.exe'" | Select-Object ProcessId,CommandLine

Linux/macOS:

# List all kubectl processes
ps aux | grep kubectl

# See the exact commands with process IDs
ps -ef | grep kubectl

This helps you:

• Verify port-forwards are actually running
• See the exact namespaces and ports being used
• Identify stuck processes that might need manual termination
• Debug connectivity issues by examining the actual commands

Common troubleshooting

• Port already in use: Check if another kubectl process is using the same local port
• Connection refused: Verify the namespace and service name in the command line
• Stuck processes: Use the process IDs to manually terminate if needed (taskkill /PID <pid> on Windows, kill <pid> on Linux/macOS)

Tools

• list_k8s_namespaces

  • Title: List namespaces
  • Description: List all available Kubernetes namespaces.
  • Parameters: None
  • Read-only: true

• list_k8s_services

  • Title: List services
  • Description: List available services grouped by short name and environment.
  • Parameters:
    • namespace (string, optional): Filter results to a namespace.
  • Read-only: true

• start_k8s_port_forward

  • Title: Start port-forward
  • Description: Start port-forwarding for one or more services.
  • Parameters:
    • services (array, required): List of service configs.
      • serviceName (string, required): Short name of the service. (Call list_k8s_services first.)
      • localPort (number, required): Local port to bind (1-65535).
      • namespace (string, optional): Namespace to target.
      • remotePort (number, optional): Remote (cluster) port.
      • environment (string, optional): dev | qa | stg | prod.
      • includeLogs (boolean, optional): Whether to open logs in a separate window (default: true).
  • Read-only: false

• stop_k8s_port_forward

  • Title: Stop port-forward
  • Description: Stop all active port-forward processes started by this MCP server.
  • Parameters: None
  • Read-only: false

Workflow

1. Call list_k8s_services (and optionally list_k8s_namespaces) to discover exact service short names and namespaces.
2. Call start_k8s_port_forward with services set to an array of configs (one per service).
3. When done, call stop_k8s_port_forward.

Examples

Quick reference (natural language → tool calls)

• "Run api and auth services on local ports 3002, 3003" → list_k8s_services({}) then start_k8s_port_forward({ services: [{ serviceName: "api", localPort: 3002 }, { serviceName: "auth", localPort: 3003 }] })

• "Run order service from shared services namespace in local port 3000" → start_k8s_port_forward({ services: [{ serviceName: "order", namespace: "shared-services", localPort: 3000 }] })

• "Run order service from shared services namespace and remote port 3000 in local port 3000" → start_k8s_port_forward({ services: [{ serviceName: "order", namespace: "shared-services", localPort: 3000, remotePort: 3000 }] })

• "Run api service in qa environment on local port 3001" → start_k8s_port_forward({ services: [{ serviceName: "api", environment: "qa", localPort: 3001 }] })

• "Run auth service in prod environment from production namespace on local port 3002" → start_k8s_port_forward({ services: [{ serviceName: "auth", environment: "prod", namespace: "production", localPort: 3002 }] })

• "Stop all port-forwards" → stop_k8s_port_forward({})

Detailed workflow example

User: "Run the frontend service in qa on port 3001"

AI workflow:

1. Call list_k8s_services({}) to find the exact service name.
2. Receive list containing e.g. frontend: qa (ns: ...), dev (ns: ...).
3. Call start_k8s_port_forward({ services: [{ serviceName: "frontend", environment: "qa", localPort: 3001 }] }).
4. Result: port-forwards run in the MCP server process; if includeLogs is true, logs open in a separate window. The tool result includes http://localhost:3001 and the exact kubectl commands.

cd k8s-port-forward-mcp
npm install
npm run build

Then reference the built entrypoint in your MCP config:

{
  "mcpServers": {
    "k8s-port-forward": {
      "command": "node",
      "args": ["/path/to/k8s-port-forward-mcp/dist/index.js"]
    }
  }
}