# GCP Environment Intelligence MCP Tool

**Tool ID**: gcp_environment  
**Type**: mcpgcp_environment  
**Purpose**: Comprehensive Google Cloud Platform environment analysis and optimization for AI agents

## Overview

This tool enables AI agents to analyze and understand their Google Cloud Platform runtime environment, providing comprehensive insights for optimization and improvement recommendations. It's designed for agents to perform self-inspection and propose improvements to their own cloud infrastructure.

## Core Capabilities

### 🔍 **Environment Detection**
- Detect GCP platform type (Cloud Run, Compute Engine, GKE, App Engine)
- Identify project, region, zone, and service configuration
- Analyze service account permissions and IAM roles
- Detect container runtime and orchestration environment

### 📊 **Resource Analysis**
- Comprehensive compute resource inventory and utilization
- Storage resource analysis and optimization opportunities
- Network configuration and performance assessment
- Multi-zone and multi-region resource distribution

### 💰 **Cost Intelligence**
- Current month spending analysis and forecasting
- Cost breakdown by service and resource type
- Spending trend analysis and anomaly detection
- ROI-based optimization recommendations with savings estimates

### 🔒 **Security Assessment**
- IAM permissions audit and least-privilege recommendations
- Security findings identification and risk classification
- Compliance status assessment (SOC2, GDPR, HIPAA)
- Network security and firewall configuration review

### ⚡ **Performance Monitoring**
- Real-time performance metrics and historical trends
- Resource utilization analysis (CPU, memory, network)
- Application performance insights and bottleneck identification
- SLA/SLO monitoring and alerting recommendations

### 🎯 **AI-Powered Optimization**
- Machine learning-driven optimization recommendations
- Automated cost-performance trade-off analysis
- Predictive scaling and capacity planning
- Continuous improvement suggestions based on usage patterns

---

## Available Methods

### 1. `analyze_environment`
**Purpose**: Comprehensive analysis of the GCP environment
**Returns**: Complete environment analysis with all insights

**Parameters**:
- `include_costs` (boolean, default: true): Include cost analysis
- `include_security` (boolean, default: true): Include security assessment  
- `include_performance` (boolean, default: true): Include performance metrics
- `include_recommendations` (boolean, default: true): Include optimization recommendations

**Intent Example**:
```json
{
  "tool": "gcp_environment",
  "intent": "Perform a comprehensive analysis of my GCP environment including costs, security, and performance with optimization recommendations",
  "context": "I want to understand my complete infrastructure setup and get actionable improvement suggestions"
}
```

**Sample Response**:
```json
{
  "environment": "gcp",
  "metadata": {
    "platform": "cloud_run",
    "project_id": "my-project-123",
    "region": "us-central1",
    "service_account": "my-service@my-project-123.iam.gserviceaccount.com"
  },
  "compute_resources": {
    "total_running_instances": 3,
    "total_vcpus": 8,
    "total_memory_gb": 32
  },
  "cost_analysis": {
    "current_month_cost": 150.25,
    "predicted_month_cost": 180.30,
    "top_services": [
      {"service": "Compute Engine", "cost": 89.50},
      {"service": "Cloud Storage", "cost": 25.75}
    ]
  },
  "optimization_recommendations": [
    {
      "category": "compute",
      "priority": "high",
      "title": "Right-size compute instances",
      "estimated_savings": "25-45%",
      "implementation": "Downgrade to smaller machine types based on utilization"
    }
  ]
}
```

### 2. `get_environment_summary`
**Purpose**: Quick overview of the GCP environment
**Returns**: Essential environment information

**Intent Example**:
```json
{
  "tool": "gcp_environment", 
  "intent": "Give me a quick summary of what GCP environment I'm running in",
  "context": "I need to understand my basic environment setup quickly"
}
```

**Sample Response**:
```json
{
  "platform": "cloud_run",
  "project_id": "my-project-123",
  "region": "us-central1", 
  "zone": "us-central1-a",
  "service_account": "my-service@my-project-123.iam.gserviceaccount.com",
  "gcp_environment": true
}
```

### 3. `get_optimization_recommendations`
**Purpose**: AI-powered optimization suggestions
**Returns**: Prioritized recommendations with implementation guidance

**Intent Example**:
```json
{
  "tool": "gcp_environment",
  "intent": "What optimizations can I make to improve my GCP environment?",
  "context": "I want specific recommendations to reduce costs and improve performance"
}
```

### 4. `get_cost_analysis`
**Purpose**: Detailed cost breakdown and forecasting
**Returns**: Cost metrics, trends, and optimization opportunities

**Intent Example**:
```json
{
  "tool": "gcp_environment",
  "intent": "Analyze my GCP costs and spending patterns",
  "context": "I need to understand where money is being spent and how to optimize costs"
}
```

### 5. `get_security_assessment`
**Purpose**: Security posture evaluation
**Returns**: Security findings, compliance status, and recommendations

**Intent Example**:
```json
{
  "tool": "gcp_environment",
  "intent": "Assess the security of my GCP environment",
  "context": "I want to understand security risks and compliance status"
}
```

### 6. `get_performance_metrics`
**Purpose**: Performance monitoring and analysis
**Returns**: Performance metrics, utilization data, and optimization suggestions

**Intent Example**:
```json
{
  "tool": "gcp_environment",
  "intent": "Show me performance metrics for my GCP resources",
  "context": "I need to understand how my infrastructure is performing"
}
```

### 7. `detect_platform`
**Purpose**: Platform detection and configuration analysis
**Returns**: Platform type, configuration details, and setup recommendations

**Intent Example**:
```json
{
  "tool": "gcp_environment",
  "intent": "What GCP platform am I running on and how is it configured?",
  "context": "I want to understand my deployment architecture and platform specifics"
}
```

---

## Intelligence Features

### 🧠 **Self-Optimization Intelligence**
The tool enables agents to analyze and optimize their own runtime environment:

```json
{
  "tool": "gcp_environment",
  "intent": "Analyze my own environment and suggest improvements for my AI agent workload",
  "context": "I want to optimize my own infrastructure for better performance and cost efficiency"
}
```

### 📈 **Predictive Analytics**
- Cost forecasting based on usage trends
- Performance bottleneck prediction
- Capacity planning recommendations
- Security risk trend analysis

### 🎯 **Business Impact Focus**
- ROI calculations for optimization recommendations
- Business risk assessment for security findings
- Performance impact analysis for proposed changes
- Cost-benefit analysis for infrastructure investments

### 🔄 **Continuous Improvement**
- Historical trend analysis for optimization tracking
- Success metrics for implemented recommendations
- Performance baseline establishment and monitoring
- Iterative optimization suggestion refinement

---

## Environment Support

### ✅ **Supported GCP Platforms**
- **Cloud Run**: Serverless container platform
- **Compute Engine**: Virtual machine instances
- **Google Kubernetes Engine (GKE)**: Managed Kubernetes
- **App Engine**: Platform-as-a-Service

### ✅ **Local Development**
- Environment variable detection
- Credential configuration analysis
- GCP CLI setup recommendations
- Local-to-cloud migration suggestions

### 📊 **Resource Coverage**
- Compute instances and machine types
- Storage buckets and volumes
- Network configuration and firewall rules
- Load balancers and traffic routing
- Database instances and clusters
- Monitoring and logging configuration

---

## Usage Patterns

### 🚀 **Agent Self-Assessment**
Perfect for agents that want to understand and optimize their own environment:
```json
{
  "tool": "gcp_environment",
  "intent": "Help me understand my current environment and suggest optimizations",
  "context": "I'm an AI agent running in GCP and want to optimize my own infrastructure"
}
```

### 💰 **Cost Optimization**
Ideal for identifying cost savings opportunities:
```json
{
  "tool": "gcp_environment", 
  "intent": "Find ways to reduce my GCP costs without impacting performance",
  "context": "Budget is tight and I need to optimize spending"
}
```

### 🔒 **Security Hardening**
Essential for security assessment and improvement:
```json
{
  "tool": "gcp_environment",
  "intent": "Assess my security posture and recommend improvements",
  "context": "I need to ensure my environment meets security best practices"
}
```

### ⚡ **Performance Optimization**
Critical for performance-sensitive workloads:
```json
{
  "tool": "gcp_environment",
  "intent": "Analyze my performance metrics and suggest optimizations",
  "context": "I need to improve response times and resource efficiency"
}
```

---

## Best Practices

### 🎯 **Optimization Strategy**
1. **Start with Summary**: Use `get_environment_summary` to understand current state
2. **Comprehensive Analysis**: Use `analyze_environment` for detailed insights
3. **Focus Areas**: Use specific methods for targeted analysis
4. **Implementation**: Follow recommendations with clear priorities

### 📊 **Data-Driven Decisions**
- Always base recommendations on actual metrics and usage data
- Include confidence levels and uncertainty ranges in predictions
- Provide multiple optimization scenarios (conservative, moderate, aggressive)
- Track implementation success and iterate on recommendations

### 🔄 **Continuous Monitoring**
- Regular environment health checks
- Trend analysis for proactive optimization
- Performance baseline updates
- Cost anomaly detection and alerting

### 🎓 **Learning and Adaptation**
- Learn from successful optimizations to improve future recommendations
- Adapt to changing usage patterns and business requirements
- Incorporate feedback from implementation results
- Stay updated with new GCP features and optimization opportunities

---

This tool empowers AI agents to become intelligent infrastructure optimizers, capable of understanding, analyzing, and improving their own cloud environments for maximum efficiency and effectiveness.