# That's it! Just the Kubiya CLI
curl -fsSL https://raw.githubusercontent.com/kubiyabot/cli/main/install.sh | bash

# Set your API key
export KUBIYA_API_KEY="kb-..."

# Start MCP server (runs anywhere!)
kubiya mcp serve

{
  "mcpServers": {
    "kubiya": {
      "command": "kubiya",
      "args": ["mcp", "serve"],
      "env": {
        "KUBIYA_API_KEY": "kb-your-api-key-here"
      }
    }
  }
}

{
  "mcp.servers": {
    "kubiya": {
      "command": "kubiya",
      "args": ["mcp", "serve"],
      "env": {
        "KUBIYA_API_KEY": "kb-your-api-key-here"
      }
    }
  }
}

import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import StdioServerTransport

async def use_kubiya_tools():
    # Connect to Kubiya MCP server
    server_params = StdioServerParameters(
        command="kubiya",
        args=["mcp", "serve"],
        env={"KUBIYA_API_KEY": "kb-your-key"}
    )
    
    async with StdioServerTransport(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize
            await session.initialize()
            
            # List available tools
            tools = await session.list_tools()
            print(f"Available tools: {[t.name for t in tools.tools]}")
            
            # Execute a tool
            result = await session.call_tool(
                "execute_tool",
                {
                    "tool_name": "kubectl",
                    "args": {"command": "get pods -A"},
                    "runner": "auto"
                }
            )
            print(result.content)

# Run it
asyncio.run(use_kubiya_tools())

# In Claude/ChatGPT, just say:
"Create a tool that checks our Kubernetes cluster health and restarts any failed pods"

# Kubiya will create and execute:
# - kubectl get pods --all-namespaces --field-selector=status.phase=Failed
# - kubectl delete pod <failed-pods> --grace-period=0
# - kubectl get pods --watch (to verify restart)

# Ask your AI:
"Deploy our application to staging with version 2.1.0, run tests, and promote to production if successful"

# Kubiya executes the complete workflow:
# - docker build -t app:2.1.0
# - kubectl apply -f k8s/staging/ 
# - run integration tests
# - if tests pass: kubectl apply -f k8s/production/
# - send notification to Slack

# Natural language request:
"Process the daily sales data, validate it, transform it to our schema, and load it into the warehouse"

# Kubiya handles the entire pipeline:
# - Download data from S3
# - Python/pandas data validation
# - ETL transformations  
# - Load to Snowflake/BigQuery
# - Data quality checks
# - Alerting on failures

# Enable policy enforcement
export KUBIYA_OPA_ENFORCE=true

# Create policies via CLI
kubiya policy create --name "prod-access" --file policy.rego

# Test permissions
kubiya policy test-tool --tool kubectl --args '{"command": "delete pod"}' --runner prod

# Automatic runner selection based on:
# - Health status
# - Current load  
# - Geographic location
# - Resource requirements
{
  "tool_name": "heavy-computation",
  "runner": "auto",  # Kubiya picks the best runner
  "args": {"dataset": "large"}
}

# Enable full platform capabilities  
kubiya mcp serve --allow-platform-apis

# Now AI can manage:
# - Create/delete runners
# - Manage integrations
# - Control agent deployments
# - Administer knowledge base

# Basic server
kubiya mcp serve

# With platform APIs enabled
kubiya mcp serve --allow-platform-apis

# With policy enforcement  
KUBIYA_OPA_ENFORCE=true kubiya mcp serve

# Custom configuration file
kubiya mcp serve --config ~/.kubiya/mcp-config.json

{
  "enable_runners": true,
  "allow_platform_apis": false,
  "enable_opa_policies": false,
  "allow_dynamic_tools": false,
  "verbose_logging": false,
  "whitelisted_tools": [
    {
      "name": "kubectl",
      "alias": "",
      "description": "Executes kubectl commands. For namespace-scoped resources, include '-n <namespace>' in the command. Use '--all-namespaces' for cluster-wide queries. Some resources like nodes and persistent volumes are cluster-scoped and don't require a namespace.",
      "type": "docker",
      "content": "\nset -eu\nTOKEN_LOCATION=\"/tmp/kubernetes_context_token\"\nCERT_LOCATION=\"/tmp/kubernetes_context_cert\"\n# Inject in-cluster context using the temporary token file\nif [ -f $TOKEN_LOCATION ] && [ -f $CERT_LOCATION ]; then\n    KUBE_TOKEN=$(cat $TOKEN_LOCATION)\n    kubectl config set-cluster in-cluster --server=https://kubernetes.default.svc --certificate-authority=$CERT_LOCATION > /dev/null 2>&1\n    kubectl config set-credentials in-cluster --token=$KUBE_TOKEN > /dev/null 2>&1\n    kubectl config set-context in-cluster --cluster=in-cluster --user=in-cluster > /dev/null 2>&1\n    kubectl config use-context in-cluster > /dev/null 2>&1\nelse\n    echo \"Error: Kubernetes context token or cert file not found at $TOKEN_LOCATION or $CERT_LOCATION respectively.\"\n    exit 1\nfi\n\n\n    #!/bin/bash\n    set -e\n\n    # Show the command being executed\n    echo \"🔧 Executing: kubectl $command\"\n\n    # Run the kubectl command\n    if eval \"kubectl $command\"; then\n        echo \"✅ Command executed successfully\"\n    else\n        echo \"❌ Command failed: kubectl $command\"\n        exit 1\n    fi\n    ",
      "args": [
        {
          "name": "command",
          "type": "string",
          "description": "The full kubectl command to execute. Examples include (but are not limited to):\n- 'get pods -n default'\n- 'create namespace test'\n- 'get pods --all-namespaces'\n- 'get nodes'  # cluster-scoped resource, no namespace needed\n- 'describe node my-node-1'",
          "required": true
        }
      ],
      "env": null,
      "with_files": [
        {
          "source": "/var/run/secrets/kubernetes.io/serviceaccount/token",
          "destination": "/tmp/kubernetes_context_token"
        },
        {
          "source": "/var/run/secrets/kubernetes.io/serviceaccount/ca.crt",
          "destination": "/tmp/kubernetes_context_cert"
        }
      ],
      "with_volumes": null,
      "icon_url": "https://kubernetes.io/icons/icon-128x128.png",
      "image": "kubiya/kubectl-light:latest",
      "mermaid": "graph TD\n    %% Styles\n    classDef triggerClass fill:#3498db,color:#fff,stroke:#2980b9,stroke-width:2px,font-weight:bold\n    classDef paramClass fill:#2ecc71,color:#fff,stroke:#27ae60,stroke-width:2px\n    classDef execClass fill:#e74c3c,color:#fff,stroke:#c0392b,stroke-width:2px,font-weight:bold\n    classDef envClass fill:#f39c12,color:#fff,stroke:#f1c40f,stroke-width:2px\n\n    %% Main Components\n    Trigger(\"Trigger\"):::triggerClass\n    Params(\"Parameters\"):::paramClass\n    Exec(\"kubectl\"):::execClass\n    Env(\"Environment\"):::envClass\n\n    %% Flow\n    Trigger --> Params --> Exec\n    Env --> Exec\n\n    %% Trigger Options\n    User(\"User\")\n    API(\"API\")\n    Webhook(\"Webhook\")\n    Cron(\"Scheduled\")\n    User --> Trigger\n    API --> Trigger\n    Webhook --> Trigger\n    Cron --> Trigger\n\n    %% Parameters\n    subgraph Parameters[\"Parameters\"]\n        direction TB\n        Param0(\"command (Required)<br/>The full kubectl command to execute. Examples include (but are not limited to):<br/>- 'get pods -n default'<br/>- 'create namespace test'<br/>- 'get pods --all-namespaces'<br/>- 'get nodes'  # cluster-scoped resource, no namespace needed<br/>- 'describe node my-node-1'<br/>Type: string\"):::paramClass\n    end\n    Parameters --- Params\n\n    %% Execution\n    subgraph Execution[\"Execution\"]\n        direction TB\n        Code(\"Script: <br/>set -eu<br/>TOKEN_LOCATION=\\\"/tmp/kubernetes_context_t...\")\n        Type(\"Type: Docker\")\n        Image(\"Docker Image: kubiya/kubectl-light:latest\")\n    end\n    Execution --- Exec\n\n    %% Environment\n    subgraph Environment[\"Environment\"]\n        direction TB\n    end\n    Environment --- Env\n\n    %% Context Note\n    ContextNote(\"Parameter values can be<br/>fetched from context<br/>based on the trigger\")\n    ContextNote -.-> Params",
      "runner": "core-testing-2"
    }
  ]
}