Kubernetes Diagnostics MCP Server

A Model Context Protocol (MCP) server specifically designed for intelligent Kubernetes troubleshooting and diagnostics. Built to complement existing K8s management tools with advanced debugging capabilities.

🚀 Features

Advanced Diagnostics

• Pod Health Analysis: Deep inspection of pod issues with intelligent suggestions
• Cluster Health Overview: Comprehensive cluster health assessment
• Log Analysis: Automated error pattern detection in pod logs
• Workload Recommendations: Best practice suggestions for deployments and services

Intelligent Troubleshooting

• Identifies common issues: CrashLoopBackOff, ImagePullBackOff, resource constraints
• Provides contextual suggestions based on error patterns
• Analyzes restart counts, resource usage, and configuration issues
• Correlates events with pod problems

Built for Production

• Works with any Kubernetes cluster (in-cluster or external)
• Supports both kubeconfig and in-cluster authentication
• Comprehensive error handling and logging
• Follows Kubernetes client-go best practices

🛠 Installation

Prerequisites

• Go 1.21+
• Access to a Kubernetes cluster
• kubectl configured or running inside a K8s cluster

Build from Source

git clone <this-repo>
cd k8s-diagnostics-mcp
go mod tidy
go build -o k8s-diagnostics-mcp

Configuration

The server automatically detects your Kubernetes configuration:

1. In-cluster: Uses service account when running inside K8s
2. Local: Uses ~/.kube/config or $KUBECONFIG environment variable

🔧 Available Tools

diagnose_pod

Diagnose issues with a specific Kubernetes pod.

Parameters:

• pod_name (required): Name of the pod to diagnose
• namespace (optional): Kubernetes namespace (default: "default")

Returns:

• Pod status and phase information
• Container restart counts and ready status
• Identified issues and intelligent suggestions
• Recent events related to the pod
• Resource configuration analysis

analyze_cluster_health

Analyze overall Kubernetes cluster health and identify issues.

Returns:

• Node count and health status
• Namespace count
• List of problematic pods with diagnostics
• Resource usage overview
• Cluster-wide recommendations

get_workload_recommendations

Get recommendations for improving workload configurations.

Parameters:

• namespace (optional): Kubernetes namespace to analyze (default: "default")

Returns:

• Best practice recommendations for deployments
• Resource limit suggestions
• High availability recommendations

analyze_pod_logs

Get and analyze pod logs for common error patterns.

Parameters:

• pod_name (required): Name of the pod
• namespace (optional): Kubernetes namespace (default: "default")
• container (optional): Specific container name
• lines (optional): Number of log lines to retrieve (default: 100)

Returns:

• Raw log output
• Detected error patterns
• Contextual suggestions based on errors
• Log analysis summary

📚 Resources

k8s://troubleshooting/common-issues

Provides a comprehensive troubleshooting guide for common Kubernetes issues including:

• CrashLoopBackOff debugging
• ImagePullBackOff resolution
• Pending pod issues
• Performance troubleshooting
• Networking problem diagnosis

🎯 Use Cases

For DevOps Engineers

• Quick pod issue diagnosis during incidents
• Cluster health monitoring and alerting
• Automated troubleshooting workflows
• Best practice compliance checking

For Platform Teams

• Standardized troubleshooting procedures
• Knowledge sharing through AI-assisted debugging
• Proactive issue identification
• Developer self-service debugging

For AI-Assisted Operations

• Integration with ChatGPT, Claude, or other LLMs
• Natural language troubleshooting queries
• Automated incident response
• Intelligent alert correlation

🌟 What Makes This Unique

Unlike existing Kubernetes MCP servers that focus on basic cluster management, this server specializes in:

1. Intelligent Issue Detection: Goes beyond simple status checks to identify root causes
2. Contextual Suggestions: Provides actionable recommendations based on specific error patterns
3. Log Analysis: Automatically parses and analyzes logs for common issues
4. Production-Ready: Built with enterprise Kubernetes environments in mind
5. CNCF Ecosystem Integration: Designed to work with other CNCF tools and best practices

🤝 Contributing

We welcome contributions! This project aligns with our commitment to the CNCF ecosystem and open-source community.

Development

# Run locally
go run main.go

# Run tests
go test ./...

# Build for production
go build -ldflags="-s -w" -o k8s-diagnostics-mcp

📞 Support

• Project Blog: Check our technical blog for K8s best practices
• GitHub Issues: Report bugs and feature requests
• CNCF Community: Engage with the broader Kubernetes community

🏷 License

[TODO]

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Built with ❤️ for the CNCF community