Terragrunt GCP MCP Tool

A Model Context Protocol (MCP) server tool for managing Google Cloud Platform infrastructure using Terragrunt. This tool provides intelligent automation and management capabilities for the terragrunt-gcp-org-automation codebase with support for Terragrunt Experimental Features.

Features

🧪 Experimental Features Support

• Terragrunt Stacks: Enhanced dependency management and parallel execution using the experimental stacks feature
• Enhanced Dependency Resolution: Improved dependency analysis and execution ordering
• Stack-Level Operations: Execute commands across multiple units with intelligent dependency handling
• Parallel Execution: Run multiple units in parallel within stacks for faster deployments
• Stack Outputs: Aggregate and manage outputs at the stack level

Resource Management

• Add New Resources: Create new infrastructure resources with templates
• Query Resources: Inspect existing resource configurations and states
• Update Resources: Modify resource configurations safely
• Delete Resources: Remove resources with dependency checking

Deployment Operations

• Check Status: Monitor deployment status and health
• Trigger Deployments: Initiate infrastructure deployments
• Plan Changes: Generate and review infrastructure plans
• Rollback: Revert failed or unwanted changes

Monitoring & Notifications

• Status Monitoring: Track infrastructure state and changes
• Team Notifications: Send updates to Slack channels
• Alert Management: Configure and manage infrastructure alerts
• Audit Logging: Track all operations and changes

Advanced Features

• Dependency Analysis: Understand resource dependencies
• Environment Management: Handle dev/staging/production environments
• Template Management: Create and manage infrastructure templates
• Cost Analysis: Monitor and optimize infrastructure costs

Terragrunt CLI Redesign Support

• New Command Structure: Uses the latest Terragrunt CLI redesign with run commands
• Environment Variables: Updated to use TG_ prefixed environment variables
• Backend Bootstrap: Automatic backend resource provisioning with --backend-bootstrap
• Dependency Discovery: Enhanced dependency graph analysis with find and list commands
• Run All Operations: Support for run --all commands across multiple units

🤖 AutoDevOps Assistant Integration (New!)

This tool now includes comprehensive system prompts for creating AutoDevOps assistants that can help manage, monitor, and maintain cloud infrastructure using all the tool's capabilities.

Getting AutoDevOps System Prompts

# Get the compact system prompt (recommended for most use cases)
python3 -m terragrunt_gcp_mcp.cli get-autodevops-prompt

# Get the extended prompt with full details
python3 -m terragrunt_gcp_mcp.cli get-autodevops-prompt --variant extended

# Get CLI-specific prompt for automation
python3 -m terragrunt_gcp_mcp.cli get-autodevops-prompt --variant cli

# Get prompt in JSON format for API integration
python3 -m terragrunt_gcp_mcp.cli get-autodevops-prompt --format json

# Save prompt to file for integration
python3 -m terragrunt_gcp_mcp.cli get-autodevops-prompt --output-file autodevops_prompt.txt

# Get full context including capabilities and tools
python3 -m terragrunt_gcp_mcp.cli get-autodevops-prompt --format context

MCP Tool Access

# Get system prompt via MCP tool
result = get_autodevops_system_prompt(
    variant="compact",  # "compact", "extended", "cli"
    format="json"       # "text", "json", "context"
)

# Use in conversation systems
from terragrunt_gcp_mcp.autodevops_prompt import inject_system_prompt

conversation = [
    {"role": "user", "content": "Help me understand my infrastructure"}
]

# Inject AutoDevOps system prompt
conversation_with_prompt = inject_system_prompt(conversation, variant="compact")

Claude Desktop Integration

Add the AutoDevOps prompt to your Claude Desktop configuration:

{
  "mcpServers": {
    "terragrunt-gcp-autodevops": {
      "command": "python3",
      "args": [
        "/Users/spol/Desktop/GIT/INTI/terragrunt-gcp-tool-mcp/run_server.py",
        "/Users/spol/Desktop/GIT/INTI/terragrunt-gcp-tool-mcp/config/config.yaml"
      ],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/your/gcp-credentials.json"
      }
    }
  },
  "global": {
    "systemPrompt": "You are an AutoDevOps Infrastructure Assistant with expert knowledge in cloud infrastructure management. You have access to the Terragrunt GCP MCP Tool with comprehensive capabilities for managing Google Cloud Platform infrastructure. ROLE: Help users manage, monitor, and maintain cloud infrastructure efficiently and safely. Always start with visualization using draw_resource_tree or visualize_infrastructure, validate before deployment with validate_resource_config, and follow GitOps best practices."
  }
}

API Integration Example

import openai
from terragrunt_gcp_mcp.autodevops_prompt import get_system_prompt

# Initialize with AutoDevOps system prompt
client = openai.OpenAI()

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {
            "role": "system", 
            "content": get_system_prompt("compact")
        },
        {
            "role": "user", 
            "content": "Show me the current state of our dev-99 environment and suggest optimizations"
        }
    ]
)

Automation Integration

#!/bin/bash
# Get AutoDevOps prompt for automation scripts
PROMPT=$(python3 -m terragrunt_gcp_mcp.cli get-autodevops-prompt --variant cli --format text)

# Use in CI/CD pipelines, monitoring scripts, etc.
echo "AutoDevOps Assistant initialized with capabilities:"
echo "$PROMPT" | grep -A 20 "Available Commands"

What the AutoDevOps Assistant Provides

Core Capabilities:

• 🌳 Infrastructure Visualization: Visual trees and dependency graphs
• 🧪 Experimental Stacks: Parallel execution and enhanced dependency management
• 🚀 Safe Deployments: Validation, planning, and rollback capabilities
• 🔧 Resource Management: Complete lifecycle management with safety checks
• 📊 Monitoring: Health assessment and performance optimization
• 🔐 Security: Compliance and audit trail management

Operational Guidelines:

• Safety-First Approach: Always validate → visualize → plan → deploy
• Visualization-First: Start with tree/graph visualization before making changes
• GitOps Best Practices: Version control integration and automated testing
• Environment Protection: Special handling for production environments

Response Pattern:

1. Current State Visualization - Show infrastructure state with tree/graph tools
2. Impact Analysis - Analyze what would change and what might be affected
3. Recommendations - Provide best practices and suggested actions
4. Implementation Steps - Clear, actionable steps with specific tool commands
5. Validation & Monitoring - How to verify success and ongoing monitoring

🌳 Resource Tree Visualization

Installation

# Clone the repository
git clone <repository-url>
cd terragrunt-gcp-tool-mcp

# Install dependencies
pip install -r requirements.txt

# Or install in development mode
pip install -e .

# Configure the MCP server
cp config/config.example.yaml config/config.yaml
# Edit config.yaml with your settings

Configuration

Create a config/config.yaml file with your settings:

# GCP Configuration
gcp:
  project_id: "your-project-id"
  credentials_path: "/path/to/credentials.json"
  default_region: "europe-west2"

# Terragrunt Configuration
terragrunt:
  root_path: "/path/to/terragrunt-gcp-org-automation"
  binary_path: "terragrunt"
  terraform_binary: "tofu"
  
  # Experimental features configuration
  experimental:
    # Stacks feature (experimental)
    stacks_enabled: true                    # Enable Terragrunt stacks experimental feature
    enhanced_dependency_resolution: true    # Use enhanced dependency resolution
    parallel_execution: true                # Enable parallel execution within stacks
    stack_outputs: true                     # Enable stack-level outputs
    recursive_stacks: false                 # Enable recursive stacks (not yet stable)
    
    # Stack execution settings
    max_parallel_units: 10                  # Maximum number of units to execute in parallel
    stack_timeout: 7200                     # Timeout for stack operations in seconds (2 hours)
    continue_on_error: false                # Continue stack execution on unit errors

# Slack Configuration (optional)
slack:
  webhook_url: "https://hooks.slack.com/..."
  default_channel: "#infrastructure"

# Monitoring Configuration
monitoring:
  enabled: true
  check_interval: 300  # seconds

Usage

Running as MCP Server

There are several ways to start the MCP server:

Option 1: Using the startup script (Recommended)

# Start the MCP server with default config
python3 run_server.py

# Start with custom config file
python3 run_server.py config/config.yaml

Option 2: Using the module directly

# Start the MCP server
python3 -m terragrunt_gcp_mcp.server

# With custom config
python3 -m terragrunt_gcp_mcp.server config/config.yaml

Option 3: Using the CLI

# Initialize configuration first
python3 -m terragrunt_gcp_mcp.cli init

# Start the server
python3 -m terragrunt_gcp_mcp.cli server

# Other CLI commands
python3 -m terragrunt_gcp_mcp.cli list-resources
python3 -m terragrunt_gcp_mcp.cli status

The server will be available for MCP clients to connect via stdio.

Using with Claude Desktop

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "terragrunt-gcp": {
      "command": "python3",
      "args": ["-m", "terragrunt_gcp_mcp.server"],
      "cwd": "/path/to/terragrunt-gcp-tool-mcp"
    }
  }
}

Available Tools

The MCP server provides these tools:

🧪 Experimental Stacks Tools

• list_stacks - List all Terragrunt stacks using experimental features
• get_stack_details - Get detailed information about a specific stack including units and execution order
• execute_stack_command - Execute commands on stacks with parallel execution and dependency management
• get_stack_outputs - Get aggregated outputs from stack-level operations
• get_enhanced_infrastructure_status - Get comprehensive status including both traditional resources and stacks

Resource Management

• list_resources - List all resources in the infrastructure
• get_resource - Get detailed information about a specific resource (deprecated, use get_resource_details)
• get_resource_details - Get comprehensive information about a specific resource including attributes, state, and configuration
• validate_resource_config - Validate a Terragrunt resource configuration with dependency checking
• create_resource - Create a new infrastructure resource
• update_resource - Update an existing resource configuration
• delete_resource - Delete a resource (with dependency checking)

Visualization & Tree Drawing (New!)

• draw_resource_tree - Draw a visual resource tree using Terragrunt CLI redesign commands
• get_dependency_graph - Generate dependency graphs in DOT, Mermaid, or JSON format
• visualize_infrastructure - Comprehensive infrastructure visualization combining trees and graphs

AutoDevOps Assistant Integration (New!)

• get_autodevops_system_prompt - Get system prompts for LLM integration to create AutoDevOps assistants
• Support for compact, extended, and CLI-specific prompt variants
• JSON, text, and context output formats for different integration scenarios
• Built-in integration guides for Claude Desktop, APIs, and automation tools

Cost Management & Analysis (New!)

• get_cost_analysis - Comprehensive cost analysis with forecasting and optimization recommendations
• get_cost_alerts - Cost alerts based on budget thresholds and spending patterns
• get_cost_optimization_score - Infrastructure cost optimization scoring and recommendations
• get_cost_status - Complete cost status dashboard with analysis, alerts, and optimization insights
• Support for environment filtering, multiple time periods, and detailed cost breakdowns
• Integration with Google Cloud Billing API for accurate cost data

Deployment Operations

• plan_deployment - Generate deployment plan for changes (deprecated, use plan_resource_deployment)
• plan_resource_deployment - Generate a comprehensive deployment plan for a Terragrunt resource
• deploy_resources - Deploy infrastructure changes (deprecated, use apply_resource_deployment)
• apply_resource_deployment - Apply changes to a Terragrunt resource with safety checks
• check_deployment_status - Check status of ongoing deployments
• rollback_deployment - Rollback a failed deployment

Monitoring & Analysis

• get_infrastructure_status - Get overall infrastructure health
• analyze_dependencies - Analyze resource dependencies
• get_cost_analysis - Get infrastructure cost breakdown
• check_drift - Detect configuration drift

Team Collaboration

• send_slack_notification - Send notifications to team
• create_deployment_summary - Generate deployment reports
• get_audit_log - Retrieve audit logs

Examples

🧪 Using Experimental Stacks Features

CLI Usage

# List all stacks
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml list-stacks

# Get detailed information about a specific stack
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml get-stack-details "live/dev-account/test-dev/dev-99"

# Execute a command on a stack (with parallel execution)
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml execute-stack-command "live/dev-account/test-dev/dev-99" plan --dry-run

# Get stack outputs
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml get-stack-outputs "live/dev-account/test-dev/dev-99"

# Get enhanced infrastructure status including stacks
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml status --include-stacks

🌳 Resource Tree Visualization (New!)

CLI Usage

# Draw a basic resource tree
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml draw-tree

# Draw tree for specific environment
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml draw-tree --environment dev-99

# Draw tree with limited depth
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml draw-tree --max-depth 3

# Generate dependency graph in DOT format (for Graphviz)
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml dependency-graph --format dot

# Generate dependency graph in Mermaid format
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml dependency-graph --format mermaid

# Comprehensive visualization
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml visualize --type tree --format ascii
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml visualize --type dag --format mermaid

# Get tree data in JSON format
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml draw-tree --format json

MCP Usage

# Example: Draw a resource tree
result = draw_resource_tree(
    environment="dev-99",
    format="tree",
    include_dependencies=True,
    max_depth=4
)

# Example: Generate dependency graph
result = get_dependency_graph(
    environment="dev-99",
    output_format="mermaid"
)

# Example: Comprehensive visualization
result = visualize_infrastructure(
    environment="dev-99",
    visualization_type="tree",
    include_dependencies=True,
    output_format="ascii"
)

# Example: List all stacks (experimental)
result = list_stacks(environment="dev-99")

# Example: Get stack details with units and execution order (experimental)
result = get_stack_details(stack_path="live/dev-account/test-dev/dev-99")

# Example: Execute a plan command on a stack (experimental)
result = execute_stack_command(
    stack_path="live/dev-account/test-dev/dev-99",
    command="plan",
    dry_run=True
)

# Example: Get enhanced status including stacks (experimental)
result = get_enhanced_infrastructure_status(
    environment="dev-99",
    include_stacks=True,
    include_costs=False
)

Example Tree Output

Infrastructure
├── dev-account
│   └── test-dev
│       └── dev-99 (folder)
│           ├── project (project)
│           ├── europe-west2 (folder)
│           │   ├── vpc (vpc-network) [deps: 1]
│           │   ├── compute (folder)
│           │   │   ├── web-server-01 (compute) [deps: 2]
│           │   │   └── sftp-worker-01 (compute) [deps: 2]
│           │   └── secrets (folder)
│           │       └── sftp-sshfs-host (secrets) [deps: 1]
│           └── bigquery (folder)
│               └── dataset-01 (bigquery) [deps: 1]

Example Dependency Graph (Mermaid)

graph TD
  project[project] --> vpc[vpc]
  vpc[vpc] --> web_server_01[web-server-01]
  vpc[vpc] --> sftp_worker_01[sftp-worker-01]
  project[project] --> secrets[secrets]

Validating Resources

CLI Usage

# Validate a resource with dependency checking
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml validate-resource "web-server-01"

# Validate without checking dependencies
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml validate-resource "web-server-01" --no-check-dependencies

# Get validation results in JSON format
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml validate-resource "web-server-01" --format json

# New CLI redesign commands
# Find and discover Terragrunt configurations (replaces output-module-groups)
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml find --dag --json --dependencies

# List units with dependency information (replaces graph-dependencies)
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml list-units --dag --tree

# Generate dependency graph
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml dag-graph --format json

# Run commands across all units (uses 'run --all' internally)
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml run-all plan --environment test-dev
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml run-all apply --dry-run

MCP Usage

# Example: Validate a resource configuration
result = validate_resource_config(
    resource_path="live/dev-account/test-dev/dev-99/europe-west2/compute/web-server-01",
    check_dependencies=True
)

Planning Deployments

CLI Usage

# Generate a deployment plan (dry-run by default)
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml plan-deployment "web-server-01"

# Generate a plan and save it for later use
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml plan-deployment "web-server-01" --save-plan

# Get plan results in JSON format
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml plan-deployment "web-server-01" --format json

MCP Usage

# Example: Generate a deployment plan
result = plan_resource_deployment(
    resource_path="live/dev-account/test-dev/dev-99/europe-west2/compute/web-server-01",
    dry_run=True,
    save_plan=True
)

Applying Deployments

CLI Usage

# Apply deployment with confirmation prompt
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml apply-deployment "web-server-01"

# Apply deployment with auto-approval (bypasses validation)
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml apply-deployment "web-server-01" --auto-approve

# Apply using a specific plan file
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml apply-deployment "web-server-01" --plan-file "tfplan"

# Apply without notifications
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml apply-deployment "web-server-01" --no-notify

MCP Usage

# Example: Apply deployment with safety checks
result = apply_resource_deployment(
    resource_path="live/dev-account/test-dev/dev-99/europe-west2/compute/web-server-01",
    auto_approve=False,
    plan_file=None,
    notify_on_completion=True
)

Complete Workflow Example

CLI Workflow

# 1. Validate the resource first
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml validate-resource "web-server-01"

# 2. Generate and review the deployment plan
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml plan-deployment "web-server-01" --save-plan

# 3. Apply the deployment
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml apply-deployment "web-server-01"

🧪 Experimental Stacks Workflow

# 1. List available stacks
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml list-stacks

# 2. Get stack details and execution order
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml get-stack-details "dev-99"

# 3. Plan the entire stack
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml execute-stack-command "dev-99" plan --dry-run

# 4. Apply the stack with parallel execution
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml execute-stack-command "dev-99" apply

MCP Workflow

# Complete deployment workflow via MCP
# 1. Validate
validation = validate_resource_config("web-server-01", check_dependencies=True)

# 2. Plan
if validation.success:
    plan = plan_resource_deployment("web-server-01", dry_run=True, save_plan=True)
    
    # 3. Apply
    if plan.success and plan.data["plan"]["changes_summary"]["has_changes"]:
        deployment = apply_resource_deployment(
            "web-server-01", 
            auto_approve=False, 
            notify_on_completion=True
        )

Querying Individual Resources

# Get detailed information about a specific resource
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml get-resource "web-server-01"

# Get resource info with full configuration
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml get-resource "live/dev-account/test-dev/dev-99/europe-west2/compute/web-server-01" --include-config

# Get resource info in JSON format
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml get-resource "sftp-worker-01" --format json

Creating a New Compute Instance

# Example: Create a new compute instance
result = create_resource(
    resource_type="compute",
    resource_name="web-server-02",
    environment="dev-99",
    region="europe-west2",
    configuration={
        "machine_type": "e2-medium",
        "disk_size_gb": 50,
        "tags": ["web-server", "api"]
    }
)

Getting Resource Details via MCP

# Example: Get detailed resource information
result = get_resource_details(
    resource_path="live/dev-account/test-dev/dev-99/europe-west2/compute/web-server-01",
    include_configuration=True
)

Deploying Changes

# Example: Deploy specific resources
result = deploy_resources(
    resources=["live/dev-account/test-dev/dev-99/europe-west2/compute/web-server-02"],
    dry_run=False,
    notify_slack=True
)

Monitoring Status

# Example: Check infrastructure status
status = get_infrastructure_status(
    environment="dev-99",
    include_costs=True,
    include_health=True
)

💰 Cost Management & Analysis (New!)

Comprehensive cost analysis, monitoring, and optimization for your GCP infrastructure.

CLI Usage

# Get comprehensive cost analysis
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml cost-analysis

# Get cost analysis for specific environment with 60-day period
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml cost-analysis --environment dev-99 --period-days 60

# Get cost analysis in JSON format without forecasting
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml cost-analysis --format json --no-forecasting

# Check cost alerts with custom threshold
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml cost-alerts --threshold 75.0

# Get cost optimization score
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml cost-optimization-score

# Get comprehensive cost status
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml cost-status --environment dev-99

# Get cost status without optimization score
python3 -m terragrunt_gcp_mcp.cli --config config/config.yaml cost-status --no-optimization

MCP Usage

# Example: Get comprehensive cost analysis
result = get_cost_analysis(
    environment="dev-99",
    period_days=30,
    include_forecasting=True,
    include_recommendations=True
)

# Example: Get cost alerts
result = get_cost_alerts(threshold_percentage=80.0)

# Example: Get cost optimization score
result = get_cost_optimization_score()

# Example: Get complete cost status
result = get_cost_status(
    environment="dev-99",
    include_alerts=True,
    include_optimization_score=True
)

Cost Analysis Features

• Service Breakdown: Costs by GCP service (Compute, Storage, Networking, etc.)
• Environment Breakdown: Costs by environment (dev, staging, production)
• Resource-Level Costs: Individual resource cost tracking
• Cost Trends: Historical cost data and trend analysis
• Forecasting: Predictive cost modeling for budget planning
• Optimization Recommendations: Actionable cost reduction suggestions
• Budget Alerts: Proactive notifications for budget thresholds
• Optimization Scoring: Infrastructure efficiency scoring (A-F grade)

live/ ├── dev-account/ │ └── test-dev/ │ └── dev-99/ │ ├── stack.hcl # Stack definition │ ├── project/ │ │ └── terragrunt.hcl # Unit 1 │ ├── compute/ │ │ └── terragrunt.hcl # Unit 2 │ └── secrets/ │ └── terragrunt.hcl # Unit 3

#### Benefits

1. **Faster Deployments**: Parallel execution of independent units
2. **Better Dependency Management**: Automatic dependency resolution
3. **Improved Reliability**: Enhanced error handling and retry mechanisms
4. **Stack-Level Visibility**: Aggregate status and outputs
5. **Simplified Operations**: Single commands for complex deployments

## Architecture

┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ MCP Client │ │ MCP Server │ │ Terragrunt │ │ (Claude) │◄──►│ (This Tool) │◄──►│ Infrastructure│ └─────────────────┘ └──────────────────┘ └─────────────────┘ │ │ ▼ ▼ ┌──────────────────┐ ┌─────────────────┐ │ External APIs │ │ 🧪 Experimental │ │ • GCP APIs │ │ Stacks │ │ • Slack API │ │ • Enhanced │ │ • GitHub API │ │ Dependencies│ └──────────────────┘ │ • Parallel │ │ Execution │ └─────────────────┘

## Security

- **Credential Management**: Supports multiple GCP authentication methods
- **Permission Validation**: Checks permissions before operations
- **Audit Logging**: All operations are logged for security auditing
- **Safe Operations**: Dry-run mode for testing changes

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

## License

GNU General Public License v3.0 - see LICENSE.md for details.

## Support

For issues and questions:
- Create an issue in the repository
- Check the documentation in `/docs`
- Review the examples in `/examples`