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K8sGPT Model Context Protocol (MCP) Server

K8sGPT provides a Model Context Protocol (MCP) server that exposes Kubernetes cluster operations as standardized tools, resources, and prompts for AI assistants like Claude, ChatGPT, and other MCP-compatible clients.

Table of Contents

What is MCP?

The Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect to external data sources and tools. K8sGPT's MCP server exposes Kubernetes operations through this standardized interface, allowing AI assistants to:

  • Analyze cluster health and issues
  • Query Kubernetes resources
  • Access pod logs and events
  • Get troubleshooting guidance
  • Manage analyzer filters

Quick Start

Start the MCP Server

Stdio mode (for local AI assistants):

k8sgpt serve --mcp

HTTP mode (for network access):

k8sgpt serve --mcp --mcp-http --mcp-port 8089

Test with curl

curl -X POST http://localhost:8089/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/list"
  }'

Server Modes

Stdio Mode (Default)

Used by local AI assistants like Claude Desktop:

k8sgpt serve --mcp

Configure in your MCP client (e.g., Claude Desktop's claude_desktop_config.json):

{
  "mcpServers": {
    "k8sgpt": {
      "command": "k8sgpt",
      "args": ["serve", "--mcp"]
    }
  }
}

HTTP Mode

Used for network access and webhooks:

k8sgpt serve --mcp --mcp-http --mcp-port 8089

The server runs in stateless mode, so no session management is required. Each request is independent.

Available Tools

The MCP server exposes 12 tools for Kubernetes operations:

Cluster Analysis

analyze

  • Analyze Kubernetes resources for issues and problems
  • Parameters:
    • namespace (optional): Namespace to analyze
    • explain (optional): Get AI explanations for issues
    • filters (optional): Comma-separated list of analyzers to use

cluster-info

  • Get Kubernetes cluster information and version

Resource Management

list-resources

  • List Kubernetes resources of a specific type
  • Parameters:
    • resourceType (required): Type of resource (pods, deployments, services, nodes, jobs, cronjobs, statefulsets, daemonsets, replicasets, configmaps, secrets, ingresses, pvcs, pvs)
    • namespace (optional): Namespace to query
    • labelSelector (optional): Label selector for filtering

get-resource

  • Get detailed information about a specific Kubernetes resource
  • Parameters:
    • resourceType (required): Type of resource
    • name (required): Resource name
    • namespace (optional): Namespace

list-namespaces

  • List all namespaces in the cluster

Debugging and Troubleshooting

get-logs

  • Get logs from a pod container
  • Parameters:
    • podName (required): Name of the pod
    • namespace (optional): Namespace
    • container (optional): Container name
    • tail (optional): Number of lines to show
    • previous (optional): Show logs from previous container instance
    • sinceSeconds (optional): Show logs from last N seconds

list-events

  • List Kubernetes events for debugging
  • Parameters:
    • namespace (optional): Namespace to query
    • involvedObjectName (optional): Filter by object name
    • involvedObjectKind (optional): Filter by object kind

Analyzer Management

list-filters

  • List all available and active analyzers/filters

add-filters

  • Add filters to enable specific analyzers
  • Parameters:
    • filters (required): Comma-separated list of analyzer names

remove-filters

  • Remove filters to disable specific analyzers
  • Parameters:
    • filters (required): Comma-separated list of analyzer names

Integrations

list-integrations

  • List available integrations (Prometheus, AWS, Keda, Kyverno, etc.)

Configuration

config

  • Configure K8sGPT settings including custom analyzers and cache

Available Resources

Resources provide read-only access to cluster information:

cluster-info

  • URI: cluster-info
  • Get information about the Kubernetes cluster

namespaces

  • URI: namespaces
  • List all namespaces in the cluster

active-filters

  • URI: active-filters
  • Get currently active analyzers/filters

Available Prompts

Prompts provide guided troubleshooting workflows:

troubleshoot-pod

  • Interactive pod debugging workflow
  • Arguments:
    • podName (required): Name of the pod to troubleshoot
    • namespace (required): Namespace of the pod

troubleshoot-deployment

  • Interactive deployment debugging workflow
  • Arguments:
    • deploymentName (required): Name of the deployment
    • namespace (required): Namespace of the deployment

troubleshoot-cluster

  • General cluster troubleshooting workflow

Usage Examples

Example 1: Analyze a Namespace

curl -X POST http://localhost:8089/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "analyze",
      "arguments": {
        "namespace": "production",
        "explain": "true"
      }
    }
  }'

Example 2: List Pods

curl -X POST http://localhost:8089/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 2,
    "method": "tools/call",
    "params": {
      "name": "list-resources",
      "arguments": {
        "resourceType": "pods",
        "namespace": "default"
      }
    }
  }'

Example 3: Get Pod Logs

curl -X POST http://localhost:8089/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 3,
    "method": "tools/call",
    "params": {
      "name": "get-logs",
      "arguments": {
        "podName": "nginx-abc123",
        "namespace": "default",
        "tail": "100"
      }
    }
  }'

Example 4: Access a Resource

curl -X POST http://localhost:8089/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 4,
    "method": "resources/read",
    "params": {
      "uri": "namespaces"
    }
  }'

Example 5: Get a Troubleshooting Prompt

curl -X POST http://localhost:8089/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 5,
    "method": "prompts/get",
    "params": {
      "name": "troubleshoot-pod",
      "arguments": {
        "podName": "nginx-abc123",
        "namespace": "default"
      }
    }
  }'

Integration with AI Assistants

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "k8sgpt": {
      "command": "k8sgpt",
      "args": ["serve", "--mcp"]
    }
  }
}

Restart Claude Desktop and you'll see k8sgpt tools available in the tool selector.

Custom MCP Clients

Any MCP-compatible client can connect to the k8sgpt server. For HTTP-based clients:

  1. Start the server: k8sgpt serve --mcp --mcp-http --mcp-port 8089
  2. Connect to: http://localhost:8089/mcp
  3. Use standard MCP protocol methods: tools/list, tools/call, resources/read, prompts/get

HTTP API Reference

Endpoint

POST http://localhost:8089/mcp
Content-Type: application/json

Request Format

All requests follow the JSON-RPC 2.0 format:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "method_name",
  "params": {
    ...
  }
}

Discovery Methods

List Tools

{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}

List Resources

{"jsonrpc": "2.0", "id": 2, "method": "resources/list"}

List Prompts

{"jsonrpc": "2.0", "id": 3, "method": "prompts/list"}

Tool Invocation

{
  "jsonrpc": "2.0",
  "id": 4,
  "method": "tools/call",
  "params": {
    "name": "tool_name",
    "arguments": {
      "arg1": "value1",
      "arg2": "value2"
    }
  }
}

Resource Access

{
  "jsonrpc": "2.0",
  "id": 5,
  "method": "resources/read",
  "params": {
    "uri": "resource_uri"
  }
}

Prompt Access

{
  "jsonrpc": "2.0",
  "id": 6,
  "method": "prompts/get",
  "params": {
    "name": "prompt_name",
    "arguments": {
      "arg1": "value1"
    }
  }
}

Response Format

Successful responses:

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    ...
  }
}

Error responses:

{
  "jsonrpc": "2.0",
  "id": 1,
  "error": {
    "code": -32600,
    "message": "Error description"
  }
}

Advanced Configuration

Custom Port

k8sgpt serve --mcp --mcp-http --mcp-port 9000

With Specific Backend

k8sgpt serve --mcp --backend openai

With Kubeconfig

k8sgpt serve --mcp --kubeconfig ~/.kube/config

Troubleshooting

Connection Issues

Verify the server is running:

curl http://localhost:8089/mcp

Permission Issues

Ensure your kubeconfig has appropriate cluster access:

kubectl cluster-info

Tool Errors

List available tools to verify names:

curl -X POST http://localhost:8089/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}'

Learn More