MCP Kubernetes Server
Community
76·Strong
Overall Trust Score
Community-maintained MCP server for Kubernetes cluster management. Enables AI models to interact with Kubernetes API for pod management, deployment orchestration, service configuration, and cluster resource monitoring. Essential for AI-powered Kubernetes operations and cloud-native application management.
kubernetes
orchestration
mcp
model-context-protocol
Trust Vector
Performance & Reliability
84
k8s api reliability90
k8s api reliability
90
Methodology
API stability analysis
Evidence
Confidence: highLast verified: 2025-11-09
resource operation success85
resource operation success
85
Methodology
Operation success testing
Evidence
Confidence: highLast verified: 2025-11-09
cluster state accuracy88
cluster state accuracy
88
Methodology
State synchronization testing
Evidence
Confidence: highLast verified: 2025-11-09
multi cluster performance78
multi cluster performance
78
Methodology
Multi-cluster performance testing
Evidence
Confidence: mediumLast verified: 2025-11-09
error recovery82
error recovery
82
Methodology
Error handling testing
Evidence
Confidence: mediumLast verified: 2025-11-09
Security
68
rbac enforcement78
rbac enforcement
78
Methodology
Authorization testing
Evidence
Confidence: highLast verified: 2025-11-09
kubeconfig exposure risk62
kubeconfig exposure risk
62
Methodology
Credential security analysis
Evidence
Confidence: highLast verified: 2025-11-09
destructive operation risk58
destructive operation risk
58
Methodology
Operation risk assessment
Evidence
Confidence: highLast verified: 2025-11-09
namespace isolation72
namespace isolation
72
Methodology
Isolation boundary testing
Evidence
Confidence: mediumLast verified: 2025-11-09
secret access risk65
secret access risk
65
Methodology
Secrets management assessment
Evidence
Confidence: highLast verified: 2025-11-09
audit logging82
audit logging
82
Methodology
Audit logging review
Evidence
Confidence: highLast verified: 2025-11-09
Privacy & Compliance
69
cluster metadata exposure66
cluster metadata exposure
66
Methodology
Data flow analysis
Evidence
Confidence: highLast verified: 2025-11-09
pod log privacy63
pod log privacy
63
Methodology
Log privacy assessment
Evidence
Confidence: highLast verified: 2025-11-09
secret exposure risk68
secret exposure risk
68
Methodology
Secret privacy assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
third party data sharing70
third party data sharing
70
Methodology
Data sharing analysis
Evidence
Confidence: highLast verified: 2025-11-09
configmap data privacy72
configmap data privacy
72
Methodology
Configuration privacy assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
Trust & Transparency
79
documentation quality75
documentation quality
75
Methodology
Documentation completeness review
Evidence
Kubernetes MCP Docs
Good documentation but community-maintained with evolving coverage
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
operation visibility85
operation visibility
85
Methodology
Logging and traceability assessment
Evidence
Confidence: highLast verified: 2025-11-09
open source transparency88
open source transparency
88
Methodology
Source code review
Evidence
Confidence: highLast verified: 2025-11-09
security best practices68
security best practices
68
Methodology
Security documentation review
Evidence
Confidence: mediumLast verified: 2025-11-09
Operational Excellence
81
ease of setup78
ease of setup
78
Methodology
Setup complexity assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
api performance82
api performance
82
Methodology
Performance benchmarking
Evidence
Kubernetes API Performance
Performance depends on cluster size and network (typically 100-500ms)
Date: 2025-11-16
Confidence: mediumLast verified: 2025-11-09
reliability85
reliability
85
Methodology
Reliability analysis
Evidence
Confidence: highLast verified: 2025-11-09
resource coverage82
resource coverage
82
Methodology
Feature coverage assessment
Evidence
Confidence: highLast verified: 2025-11-09
community support75
community support
75
Methodology
Community support assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
✨ Strengths
- •Comprehensive Kubernetes resource management and orchestration
- •Built on stable and mature Kubernetes API
- •Excellent for cloud-native application deployment automation
- •Full operation auditability through Kubernetes audit logs
- •Open source community implementation
- •Supports RBAC for granular access control
⚠️ Limitations
- •Cluster configurations and pod specs exposed to LLM provider
- •AI can delete resources and modify critical cluster configurations
- •Pod logs and Kubernetes secrets accessible if RBAC permits
- •Requires careful RBAC configuration to limit access
- •Community-maintained with variable support quality
- •ConfigMaps and secrets may contain sensitive data
📊 Metadata
license: MIT
supported platforms:
0: All platforms with kubectl
programming languages:
0: TypeScript
1: Python
mcp version: 1.0
github repo: https://github.com/modelcontextprotocol/servers
api dependency: Kubernetes API
authentication: kubeconfig, Service Account tokens
first release: 2024-11
maintained by: Community
Use Case Ratings
code generation
90
Excellent for Kubernetes manifest generation and GitOps automation
customer support
78
Good for troubleshooting Kubernetes deployments and cluster issues
content creation
58
Limited applicability; mainly for infrastructure documentation
data analysis
82
Good for analyzing cluster metrics, resource utilization, and scaling patterns
research assistant
72
Useful for researching Kubernetes patterns and cluster configurations
legal compliance
60
High risk due to cluster access; requires strict RBAC controls
healthcare
55
Risk of exposing healthcare infrastructure; not recommended without strong controls
financial analysis
63
Moderate risk for financial infrastructure management
education
92
Excellent for teaching Kubernetes, container orchestration, and cloud-native architecture
creative writing
42
Low relevance to creative writing workflows