# K8s MCP Server

Enhanced Kubernetes access with AI-friendly port-forward management supporting headless services.

✅ **Implementation Complete** - All core features have been implemented.

## Features

- **kubectl-Inspired Output Formats**: Multiple output formats (table, wide, json, yaml, custom-columns, jsonpath) for efficient token usage
- **Resource Kind Discovery**: Natural language queries to find Custom Resource Definitions and builtin resources by keywords
- **URL-based Configuration**: Share port-forward configs via GitHub Gists or local files
- **Headless Service Support**: Direct pod targeting via label selectors  
- **AI-Optimized**: Clean service/target structure for intelligent fuzzy matching
- **Multi-format Support**: YAML, JSON, TOML configuration files
- **kubectl-like Behavior**: Selects first ready pod, same as `kubectl port-forward`
- **Session Management**: In-memory port-forward tracking with list and teardown capabilities
- **Parameterless Discovery**: Easy cluster, alias, and service discovery for AI agents

## Quick Start

### 1. Install Dependencies

```bash
pip install -e .
```

### 2. Set Environment Variables

```bash
# Required: Kubernetes cluster configurations
export KUBECONFIG_URLS="dev=file:///path/to/dev.yaml prod=file:///path/to/prod.yaml"

# Optional: Cluster aliases
export KUBECONFIG_ALIASES="development=dev production=prod"

# Required: Port-forward configuration URL
export PORT_FORWARD_CONFIG_URL="file://$(pwd)/example-port-forward-config.yaml"
# Or use a GitHub Gist:
# export PORT_FORWARD_CONFIG_URL="https://gist.githubusercontent.com/user/abc123/raw/port-forward.yaml"
```

### 3. Run the Server

```bash
python main.py
```

## Usage Examples

### AI Agent Interactions

```
User: "Setup port-forward to the FRET Druid in development"
AI Agent:
1. Calls discover_port_forward_services() to understand available services
2. Maps "FRET Druid in development" → service="druid", target="fret-dev"  
3. Calls setup_port_forward_to_target("druid", "fret-dev")
4. Returns: "✅ Connected to fret-dev Druid: http://localhost:8082 → pod/druid-router-0"
```

### Direct Tool Calls

- `connect_to_service_targets("druid", ["fret-dev"])` - Connect to specific target
- `connect_to_service_targets("druid", [])` - Connect to all Druid environments
- `list_resources("prod", "pods", labels={"app": "nginx"})` - List pods with labels
- `get_resource("dev", "deployment", "my-app", "default")` - Get specific resource
- `discover_resource_kinds("dev", ["druid", "ingestion"])` - Find resource kinds by keywords
- `list_resources("dev", "pods", output_format="custom-columns", custom_columns="NAME:.metadata.name,IP:.status.podIP")` - Custom output
- `list_port_forwards()` - Show active port-forward sessions
- `teardown_port_forward("forward-id")` - Stop a port-forward session
- Supports kubectl naming variants: `"pods"`, `"pod"`, `"po"` all work

## Output Format Examples

### Token-Efficient Resource Listing

```bash
# Default table format (kubectl-like)
list_resources(cluster="dev", kind="pods", namespace="fret-dev")
# Returns clean table: NAME | READY | STATUS | RESTARTS | AGE
```

### Custom Columns for Specific Data

```bash
# Extract specific fields with JSONPath
list_resources(
    cluster="dev", 
    kind="services",
    output_format="custom-columns", 
    custom_columns="NAME:.metadata.name,TYPE:.spec.type,PORTS:.spec.ports[*].port"
)
```

### Resource Kind Discovery

```bash
# Find resource kinds by keywords
discover_resource_kinds(
    cluster="dev",
    keywords=["druid", "ingestion"],
    match_mode="OR"
)
# Returns: DruidIngestion resource kind info with all naming variants
```

## Resource Naming Conventions

The K8s MCP server follows kubectl naming conventions:

### Resource Kind Support
- **Plural forms**: `pods`, `services`, `deployments`
- **Singular forms**: `pod`, `service`, `deployment`  
- **Abbreviations**: `po`, `svc`, `deploy`, `sts` (for StatefulSet)
- **Case insensitive**: `Pod`, `POD`, `pod` all work

### Discovery vs Search
- **`discover_*`** tools find available resource kinds/types
- **`search_*`** and **`list_*`** tools find specific instances

### Example Workflow
```bash
# 1. Discover what kinds are available
discover_resource_kinds(cluster="dev", keywords=["druid"])
# Returns: DruidIngestion kind with variants ["druidingestions", "druidingestion"]

# 2. List instances using any variant
list_resources(cluster="dev", kind="druidingestions")
# or
list_resources(cluster="dev", kind="DruidIngestion")
```

## Configuration

### Port-Forward Config Format

```yaml
druid:
  fret-dev:
    k8s_cluster: "dev-us-east"
    namespace: "druid"
    labels:
      app.kubernetes.io/component: "router"
    local_port: 8082
    remote_port: 8888

grafana:
  dev:
    k8s_cluster: "dev-us-east"
    namespace: "monitoring"
    labels:
      app.kubernetes.io/name: "grafana"
    local_port: 3000
    remote_port: 3000
```

## Common Workflows

These workflow examples demonstrate how AI agents can combine multiple tools to accomplish complex Kubernetes tasks:

### 1. Cluster Overview Workflow
```
User: "Show me the status of all my clusters"
AI Agent Flow:
1. discover_clusters() → Get available clusters ["dev", "staging", "prod"]
2. For each cluster:
   - cluster_health() → Check API server connectivity
   - list_resources("cluster", "nodes") → Get node count and status
   - list_resources("cluster", "namespaces") → Get namespace overview
3. Summarize: "3 clusters online, 12 total nodes, 8 namespaces"
```

### 2. Application Debugging Workflow
```
User: "My app isn't working in staging"
AI Agent Flow:
1. list_resources("staging", "pods", labels={"app": "user-app"}) → Find app pods
2. For each pod:
   - describe_resource("staging", "pod", pod_name) → Get detailed status
   - get_logs("staging", pod_name, lines=50) → Check recent logs
   - list_resources("staging", "events", field_selector=f"involvedObject.name={pod_name}") → Get events
3. Analyze patterns: "Found 2 pods crash-looping due to missing config map 'app-config'"
```

### 3. Safe Deployment Update Workflow
```
User: "Update nginx deployment to version 1.21"
AI Agent Flow:
1. get_resource("prod", "deployment", "nginx") → Get current deployment
2. Modify image version in manifest
3. Present proposed changes: "Will update image from nginx:1.20 to nginx:1.21"
4. User confirms: "Apply it"
5. update_resource("prod", "deployment", "nginx", new_manifest) → Apply update
6. Monitor rollout status with deployment_status()
```

### 4. Resource Cleanup Workflow
```
User: "Clean up all test resources from last week"
AI Agent Flow:
1. list_resources("dev", "all", labels={"purpose": "test", "created": "2024-01-15"}) → Find test resources
2. Group by type: "Found 5 pods, 3 services, 2 configmaps"
3. For each resource:
   - Show resource details and age
4. Present deletion plan with resource details
5. User confirms: "Yes, delete them"
6. Execute deletions with proper cascade handling
```

### 5. Enhanced Port-Forward Workflow
```
User: "Setup port-forward to the FRET Druid in development"
AI Agent Flow:
1. discover_port_forward_services() → Get available services/targets
2. AI reasoning: "FRET Druid in development" → service="druid", target="fret-dev"
3. connect_to_service_targets("druid", ["fret-dev"]) → Setup to specific target
4. Return: "✅ Connected to fret-dev Druid: http://localhost:8082 → pod/druid-router-0"
```

### 6. Troubleshooting Performance Workflow
```
User: "Why is my app slow in production?"
AI Agent Flow:
1. list_resources("prod", "pods", labels={"app": "slow-app"}) → Find app pods
2. For each pod:
   - describe_resource() → Check resource limits/requests
   - get_logs(since="1h") → Check for performance-related logs
   - execute_in_pod(["top", "-b", "-n", "1"]) → Get resource usage
3. list_resources("prod", "nodes") → Check node capacity
4. query_by_labels("prod", "hpa", "app=slow-app") → Check auto-scaling
5. Analyze: "App is CPU-throttled, increase CPU requests from 100m to 250m"
```

### 7. Security Audit Workflow
```
User: "Audit RBAC permissions for the finance team"
AI Agent Flow:
1. list_resources("prod", "rolebindings") → Get all role bindings
2. list_resources("prod", "clusterrolebindings") → Get cluster-wide bindings
3. Filter by subjects containing "finance"
4. For each binding:
   - get_resource("prod", "role", role_name) → Get role permissions
5. Summarize: "Finance team has read access to secrets in finance namespace"
```

### 8. Disaster Recovery Workflow
```
User: "Restore the database from backup"
AI Agent Flow:
1. list_resources("prod", "pods", labels={"app": "database"}) → Find DB pods
2. scale_resource("prod", "deployment", "database", replicas=0) → Scale down
3. Wait for pods to terminate
4. apply_manifest("prod", restore_job_manifest) → Apply restore job
5. Monitor job status until completion
6. scale_resource("prod", "deployment", "database", replicas=1) → Scale back up
7. Verify database connectivity
```

### Best Practices for AI Agents

1. **Always Preview Changes**: Use diff/dry-run before modifications
2. **Provide Context**: Include relevant information when reporting issues
3. **Batch Operations**: Group related operations for efficiency
4. **Handle Pagination**: Be aware of large result sets
5. **Respect Cluster Boundaries**: Never assume operations across clusters

## Tool Safety Classifications

All MCP tools are classified by their safety level for AI agent operations:

### [READ-ONLY] Tools - Safe for autonomous use
These tools only read data and never modify cluster or local state:

**Resource Operations:**
- `get_resource()` - Get specific resource details
- `get_resource_yaml()` - Get resource as YAML
- `list_resources()` - List resources with formatting options
- `list_resources_yaml()` - List resources as YAML
- `query_by_labels()` - Advanced label-based queries
- `query_by_labels_yaml()` - Label queries as YAML

**Monitoring & Analysis:**
- `troubleshoot_pod()` - Comprehensive pod debugging
- `troubleshoot_pod_yaml()` - Pod debugging as YAML
- `cluster_health()` - Overall cluster health assessment
- `deployment_status()` - Deployment rollout analysis
- `deployment_status_yaml()` - Deployment analysis as YAML
- `resource_usage()` - Resource consumption analysis
- `get_logs()` - Retrieve pod logs
- `list_port_forwards()` - List active port-forward sessions

**Discovery:**
- `discover_resource_kinds()` - Find resource types by keywords
- `discover_clusters()` - List available clusters
- `discover_cluster_aliases()` - List cluster aliases
- `discover_port_forward_services()` - List port-forward configurations

### [MODIFIES STATE] Tools - Require user approval
These tools modify cluster state or local network configuration:

**Resource Management:**
- `create_resource()` - Create new Kubernetes resources
- `update_resource()` - Update existing resources
- `patch_resource()` - Partially update resources
- `scale_resource()` - Change replica counts
- `apply_manifest()` - Apply YAML manifests

**Network Operations:**
- `connect_to_service_targets()` - Connect to configured service environments
- `setup_port_forward()` - Generic port-forward setup
- `teardown_port_forward()` - Close port-forward sessions

### [DANGEROUS] Tools - Require explicit user confirmation
These tools can cause data loss or security risks:

- `delete_resource()` - **Permanently delete resources** (cannot be undone)
- `execute_in_pod()` - **Run arbitrary commands in pods** (potential security risk)

### AI Agent Safety Guidelines

1. **Autonomous Operations**: AI agents can freely use [READ-ONLY] tools
2. **State Changes**: [MODIFIES STATE] tools should require user approval
3. **High-Risk Operations**: [DANGEROUS] tools must have explicit user confirmation
4. **Error Handling**: All tools return consistent error formats for safe parsing

## Architecture

```
k8s-mcp/
├── main.py                 # MCP server entry point with tool definitions
├── config/                 # Configuration management  
│   ├── loader.py          # URL-based config loading
│   ├── manager.py         # Cluster and port-forward session management
│   └── config.py          # Environment validation and configuration
├── tools/                  # Modular tool implementations
│   ├── resources.py       # Common resource operation patterns
│   ├── formatting.py      # All resource formatting (table, YAML, JSON)
│   ├── columns.py         # Default column definitions for resources
│   ├── discovery.py       # Resource kind discovery and CRD enumeration
│   └── port_forward.py    # Port-forward specific operations
└── utils/                  # Shared utilities
    ├── helpers.py         # Common helper functions
    └── jsonpath.py        # JSONPath processing for custom columns
```

### Code Organization Principles

- **Single Responsibility**: Each module has a clear, focused purpose
- **DRY Compliance**: No duplicate implementations across files
- **Separation of Concerns**: Client management, formatting, and business logic are separated
- **Modular Design**: Tools can be imported and reused independently