# SAFLA Resources for Orchestrator Mode

## Overview

The SAFLA MCP server provides direct access to real-time system resources through the `safla://` URI scheme. These resources provide live data for orchestration decisions, monitoring, and adaptive learning.

## Core System Resources

### Configuration and Status
```typescript
// Access current SAFLA configuration
const config = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://config"
});

// Get real-time system status
const status = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://status"
});

// Monitor deployment information
const deployments = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://deployments"
});
```

### Performance and Optimization
```typescript
// Real-time performance metrics
const metrics = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://performance-metrics"
});

// AI-generated optimization recommendations
const recommendations = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://optimization-recommendations"
});

// Performance baselines for comparison
const baselines = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://performance-baselines"
});
```

## Meta-Cognitive Resources

### System Awareness and Goals
```typescript
// Current meta-cognitive state
const cognitiveState = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://meta-cognitive-state"
});

// Active and completed goals with progress
const goals = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://goals"
});

// Available strategies with performance metrics
const strategies = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://strategies"
});
```

### Learning and Adaptation
```typescript
// Learning performance and retention metrics
const learningMetrics = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://learning-metrics"
});

// System adaptation trends and behavioral evolution
const adaptationPatterns = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://adaptation-patterns"
});
```

## Agent and Session Management

### Agent Resources
```typescript
// Active agent interaction sessions
const agentSessions = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://agent-sessions"
});

// Available agent types and capabilities
const agentCapabilities = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://agent-capabilities"
});

// User sessions and access information
const userSessions = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://user-sessions"
});
```

## Testing and Quality Assurance

### Test Resources
```typescript
// Latest test execution results and reports
const testResults = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://test-results"
});

// Code coverage and test quality metrics
const testCoverage = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://test-coverage"
});

// Performance benchmark results and trends
const benchmarkResults = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://benchmark-results"
});
```

## System Operations

### Logging and Backup
```typescript
// System logs and audit trail
const systemLogs = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://system-logs"
});

// Backup and restore operation status
const backupStatus = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://backup-status"
});

// Deployment configuration templates
const deploymentTemplates = await access_mcp_resource({
  server_name: "safla",
  uri: "safla://deployment-templates"
});
```

## Resource Usage Patterns

### Orchestration Decision Making
```typescript
async function makeOrchestrationDecision(context: string) {
  // Gather comprehensive system state
  const [status, metrics, goals, strategies] = await Promise.all([
    access_mcp_resource({ server_name: "safla", uri: "safla://status" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://performance-metrics" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://goals" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://strategies" })
  ]);

  // Analyze current state and make informed decisions
  const decision = analyzeSystemState(status, metrics, goals, strategies, context);
  
  return decision;
}
```

### Adaptive Learning Integration
```typescript
async function adaptiveOrchestration() {
  // Monitor learning metrics and adaptation patterns
  const [learningMetrics, adaptationPatterns, cognitiveState] = await Promise.all([
    access_mcp_resource({ server_name: "safla", uri: "safla://learning-metrics" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://adaptation-patterns" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://meta-cognitive-state" })
  ]);

  // Adjust orchestration strategy based on learning insights
  const adaptedStrategy = adaptOrchestrationStrategy(
    learningMetrics, 
    adaptationPatterns, 
    cognitiveState
  );
  
  return adaptedStrategy;
}
```

### Performance Optimization
```typescript
async function optimizePerformance() {
  // Get performance data and optimization recommendations
  const [metrics, recommendations, baselines, benchmarks] = await Promise.all([
    access_mcp_resource({ server_name: "safla", uri: "safla://performance-metrics" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://optimization-recommendations" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://performance-baselines" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://benchmark-results" })
  ]);

  // Apply AI-generated optimizations
  const optimizations = generateOptimizations(metrics, recommendations, baselines, benchmarks);
  
  return optimizations;
}
```

### Quality Assurance Monitoring
```typescript
async function monitorQuality() {
  // Comprehensive quality monitoring
  const [testResults, coverage, systemLogs] = await Promise.all([
    access_mcp_resource({ server_name: "safla", uri: "safla://test-results" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://test-coverage" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://system-logs" })
  ]);

  // Analyze quality metrics and identify issues
  const qualityAnalysis = analyzeQualityMetrics(testResults, coverage, systemLogs);
  
  return qualityAnalysis;
}
```

## Real-Time Monitoring Patterns

### Continuous System Monitoring
```typescript
async function continuousMonitoring() {
  const monitoringInterval = setInterval(async () => {
    try {
      // Real-time system health check
      const [status, metrics, agentSessions] = await Promise.all([
        access_mcp_resource({ server_name: "safla", uri: "safla://status" }),
        access_mcp_resource({ server_name: "safla", uri: "safla://performance-metrics" }),
        access_mcp_resource({ server_name: "safla", uri: "safla://agent-sessions" })
      ]);

      // Detect anomalies and trigger responses
      const anomalies = detectAnomalies(status, metrics, agentSessions);
      if (anomalies.length > 0) {
        await handleAnomalies(anomalies);
      }

    } catch (error) {
      console.error('Monitoring error:', error);
      await handleMonitoringError(error);
    }
  }, 30000); // Monitor every 30 seconds

  return monitoringInterval;
}
```

### Predictive Analysis
```typescript
async function predictiveAnalysis() {
  // Gather historical and current data
  const [adaptationPatterns, learningMetrics, benchmarkResults] = await Promise.all([
    access_mcp_resource({ server_name: "safla", uri: "safla://adaptation-patterns" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://learning-metrics" }),
    access_mcp_resource({ server_name: "safla", uri: "safla://benchmark-results" })
  ]);

  // Predict future system behavior and needs
  const predictions = generatePredictions(adaptationPatterns, learningMetrics, benchmarkResults);
  
  // Proactively adjust orchestration strategy
  const proactiveAdjustments = generateProactiveAdjustments(predictions);
  
  return { predictions, proactiveAdjustments };
}
```

## Error Handling and Recovery

### Resource Access Error Handling
```typescript
async function safeResourceAccess(uri: string, retries: number = 3): Promise<any> {
  for (let attempt = 1; attempt <= retries; attempt++) {
    try {
      const resource = await access_mcp_resource({
        server_name: "safla",
        uri: uri
      });
      return resource;
    } catch (error) {
      console.warn(`Resource access attempt ${attempt} failed for ${uri}:`, error);
      
      if (attempt === retries) {
        // Log to system logs and use fallback
        await logResourceError(uri, error);
        return getFallbackResource(uri);
      }
      
      // Exponential backoff
      await new Promise(resolve => setTimeout(resolve, Math.pow(2, attempt) * 1000));
    }
  }
}
```

### Resource Validation
```typescript
async function validateResourceData(uri: string, data: any): Promise<boolean> {
  try {
    // Validate resource data structure and content
    const validation = validateResourceStructure(uri, data);
    
    if (!validation.isValid) {
      console.warn(`Invalid resource data for ${uri}:`, validation.errors);
      
      // Log validation issues
      const systemLogs = await access_mcp_resource({
        server_name: "safla",
        uri: "safla://system-logs"
      });
      
      await logValidationError(uri, validation.errors, systemLogs);
      return false;
    }
    
    return true;
  } catch (error) {
    console.error(`Resource validation error for ${uri}:`, error);
    return false;
  }
}
```

## Resource Caching and Optimization

### Intelligent Caching
```typescript
class ResourceCache {
  private cache = new Map<string, { data: any, timestamp: number, ttl: number }>();
  
  async getResource(uri: string, ttl: number = 30000): Promise<any> {
    const cached = this.cache.get(uri);
    const now = Date.now();
    
    // Return cached data if still valid
    if (cached && (now - cached.timestamp) < cached.ttl) {
      return cached.data;
    }
    
    // Fetch fresh data
    const data = await access_mcp_resource({
      server_name: "safla",
      uri: uri
    });
    
    // Cache with TTL
    this.cache.set(uri, { data, timestamp: now, ttl });
    
    return data;
  }
  
  invalidateCache(uri?: string): void {
    if (uri) {
      this.cache.delete(uri);
    } else {
      this.cache.clear();
    }
  }
}
```

## Integration Examples

### Complete Orchestration Workflow
```typescript
async function executeOrchestrationWorkflow(workflowId: string) {
  try {
    // Initialize with comprehensive system state
    const initialState = await gatherSystemState();
    
    // Create and configure agents based on current needs
    const agents = await createOptimalAgents(initialState);
    
    // Execute workflow with continuous monitoring
    const result = await executeWorkflowWithMonitoring(workflowId, agents, initialState);
    
    // Update learning metrics and adaptation patterns
    await updateLearningFromWorkflow(workflowId, result);
    
    return result;
  } catch (error) {
    await handleWorkflowError(workflowId, error);
    throw error;
  }
}

async function gatherSystemState() {
  const resources = [
    "safla://status",
    "safla://performance-metrics",
    "safla://goals",
    "safla://strategies",
    "safla://meta-cognitive-state",
    "safla://agent-capabilities"
  ];
  
  const data = await Promise.all(
    resources.map(uri => access_mcp_resource({ server_name: "safla", uri }))
  );
  
  return {
    status: data[0],
    metrics: data[1],
    goals: data[2],
    strategies: data[3],
    cognitiveState: data[4],
    agentCapabilities: data[5]
  };
}