# Scorer Mode MCP Tools Documentation

## Overview

The Scorer mode leverages SAFLA's comprehensive MCP server capabilities to provide quantitative evaluation, scoring, and ranking functionality. This document details the specific MCP tools and usage patterns for implementing effective scoring workflows within the aiGI framework.

## Core MCP Tools for Scoring

### 1. Performance Analysis and Scoring Tools

#### analyze_performance_bottlenecks
**Purpose**: Identify and score performance bottlenecks in the system
**Usage Pattern**: Primary tool for performance scoring analysis

```bash
# Basic performance bottleneck analysis
use_mcp_tool safla analyze_performance_bottlenecks \
  --duration_seconds=300 \
  --include_memory_profile=true

# Extended analysis for comprehensive scoring
use_mcp_tool safla analyze_performance_bottlenecks \
  --duration_seconds=600 \
  --include_memory_profile=true
```

**Scoring Applications**:
- Generate performance degradation scores
- Identify bottleneck severity ratings
- Calculate performance improvement potential scores
- Assess system efficiency metrics

#### benchmark_memory_performance
**Purpose**: Score memory subsystem performance against baselines
**Usage Pattern**: Memory efficiency scoring and comparison

```bash
# Standard memory performance scoring
use_mcp_tool safla benchmark_memory_performance \
  --test_duration=120 \
  --memory_patterns=["sequential","random","mixed"]

# Comprehensive memory scoring analysis
use_mcp_tool safla benchmark_memory_performance \
  --test_duration=300 \
  --memory_patterns=["sequential","random","mixed","sparse","dense"]
```

**Scoring Applications**:
- Memory efficiency scores (0-100 scale)
- Access pattern optimization scores
- Memory utilization effectiveness ratings
- Comparative performance rankings

#### benchmark_vector_operations
**Purpose**: Score vector operation performance and efficiency
**Usage Pattern**: Computational performance scoring

```bash
# Vector operation performance scoring
use_mcp_tool safla benchmark_vector_operations \
  --vector_count=5000 \
  --vector_dimensions=512 \
  --operations=["add","multiply","dot_product","normalize"]

# High-performance vector scoring
use_mcp_tool safla benchmark_vector_operations \
  --vector_count=10000 \
  --vector_dimensions=1024 \
  --operations=["add","multiply","dot_product","normalize","cross_product"]
```

**Scoring Applications**:
- Computational efficiency scores
- Algorithm performance rankings
- GPU acceleration effectiveness scores
- Throughput and latency measurements

#### benchmark_mcp_throughput
**Purpose**: Score MCP protocol performance and communication efficiency
**Usage Pattern**: Communication layer performance scoring

```bash
# MCP throughput scoring
use_mcp_tool safla benchmark_mcp_throughput \
  --request_count=1000 \
  --concurrent_connections=10 \
  --payload_size="medium"

# High-load MCP performance scoring
use_mcp_tool safla benchmark_mcp_throughput \
  --request_count=5000 \
  --concurrent_connections=50 \
  --payload_size="large"
```

**Scoring Applications**:
- Communication efficiency scores
- Protocol overhead assessments
- Scalability performance ratings
- Latency and throughput metrics

### 2. Goal Achievement and Progress Scoring Tools

#### evaluate_goal_progress
**Purpose**: Score goal achievement and progress metrics
**Usage Pattern**: Primary tool for goal-based scoring

```bash
# Individual goal progress scoring
use_mcp_tool safla evaluate_goal_progress \
  --goal_id="performance_optimization_q1" \
  --include_recommendations=true

# Comprehensive goal scoring analysis
use_mcp_tool safla evaluate_goal_progress \
  --include_recommendations=true
```

**Scoring Applications**:
- Goal completion percentage scores
- Progress velocity ratings
- Quality achievement scores
- Timeline adherence metrics

#### list_goals
**Purpose**: Retrieve goal data for comprehensive scoring analysis
**Usage Pattern**: Data collection for goal scoring workflows

```bash
# Active goals scoring data collection
use_mcp_tool safla list_goals \
  --status_filter="active" \
  --priority_filter="high"

# Comprehensive goal scoring dataset
use_mcp_tool safla list_goals \
  --status_filter="all" \
  --priority_filter="all"
```

**Scoring Applications**:
- Portfolio-level goal scoring
- Priority-weighted achievement scores
- Cross-goal dependency impact scores
- Strategic alignment ratings

#### get_learning_metrics
**Purpose**: Score learning and adaptation effectiveness
**Usage Pattern**: Adaptive learning performance scoring

```bash
# Learning effectiveness scoring
use_mcp_tool safla get_learning_metrics \
  --metric_type="all" \
  --time_range_hours=168

# Specific learning dimension scoring
use_mcp_tool safla get_learning_metrics \
  --metric_type="accuracy" \
  --time_range_hours=72
```

**Scoring Applications**:
- Learning rate effectiveness scores
- Knowledge retention ratings
- Adaptation speed metrics
- Improvement trajectory scores

### 3. Strategy Effectiveness Scoring Tools

#### evaluate_strategy_performance
**Purpose**: Score strategy effectiveness and implementation success
**Usage Pattern**: Strategy performance evaluation and ranking

```bash
# Strategy effectiveness scoring
use_mcp_tool safla evaluate_strategy_performance \
  --strategy_id="optimization_strategy_v2" \
  --evaluation_period_hours=168 \
  --metrics=["efficiency","quality","speed"]

# Comprehensive strategy scoring
use_mcp_tool safla evaluate_strategy_performance \
  --strategy_id="deployment_strategy_prod" \
  --evaluation_period_hours=720 \
  --metrics=["reliability","performance","cost","user_satisfaction"]
```

**Scoring Applications**:
- Strategy ROI scores
- Implementation effectiveness ratings
- Comparative strategy rankings
- Success probability assessments

#### select_optimal_strategy
**Purpose**: Score and rank strategies for optimal selection
**Usage Pattern**: Multi-criteria strategy scoring and selection

```bash
# Strategy selection scoring
use_mcp_tool safla select_optimal_strategy \
  --context="performance_optimization" \
  --constraints={"budget": 50000, "timeline": "30d"} \
  --objectives=["speed","memory","reliability"]

# Complex strategy scoring scenario
use_mcp_tool safla select_optimal_strategy \
  --context="system_scaling" \
  --constraints={"resources": "limited", "downtime": "minimal"} \
  --objectives=["scalability","cost_efficiency","maintainability","security"]
```

**Scoring Applications**:
- Multi-objective optimization scores
- Constraint satisfaction ratings
- Risk-adjusted effectiveness scores
- Strategic fit assessments

#### analyze_adaptation_patterns
**Purpose**: Score adaptation effectiveness and behavioral evolution
**Usage Pattern**: Adaptive behavior scoring and trend analysis

```bash
# Adaptation pattern scoring
use_mcp_tool safla analyze_adaptation_patterns \
  --pattern_type="performance" \
  --analysis_depth="comprehensive" \
  --time_window_days=30

# Behavioral evolution scoring
use_mcp_tool safla analyze_adaptation_patterns \
  --pattern_type="all" \
  --analysis_depth="detailed" \
  --time_window_days=90
```

**Scoring Applications**:
- Adaptation speed scores
- Behavioral consistency ratings
- Evolution effectiveness metrics
- Pattern recognition accuracy scores

### 4. Quality Assessment and Testing Scoring Tools

#### run_integration_tests
**Purpose**: Score system integration quality and test effectiveness
**Usage Pattern**: Quality assurance scoring through testing

```bash
# Integration test scoring
use_mcp_tool safla run_integration_tests \
  --test_suite="comprehensive" \
  --parallel=true \
  --verbose=true

# Targeted integration scoring
use_mcp_tool safla run_integration_tests \
  --test_suite="performance_critical" \
  --parallel=true \
  --verbose=false
```

**Scoring Applications**:
- Test pass rate scores
- Integration quality ratings
- System reliability metrics
- Regression risk assessments

#### validate_memory_operations
**Purpose**: Score memory operation correctness and efficiency
**Usage Pattern**: Memory subsystem quality scoring

```bash
# Memory operation validation scoring
use_mcp_tool safla validate_memory_operations \
  --test_data_size=50 \
  --include_stress_test=true

# Comprehensive memory quality scoring
use_mcp_tool safla validate_memory_operations \
  --test_data_size=100 \
  --include_stress_test=true
```

**Scoring Applications**:
- Memory operation accuracy scores
- Data integrity ratings
- Stress test resilience metrics
- Memory leak detection scores

#### test_mcp_connectivity
**Purpose**: Score MCP protocol compliance and connectivity quality
**Usage Pattern**: Communication layer quality scoring

```bash
# MCP connectivity quality scoring
use_mcp_tool safla test_mcp_connectivity \
  --target_server="all" \
  --test_depth="comprehensive"

# Specific server connectivity scoring
use_mcp_tool safla test_mcp_connectivity \
  --target_server="production_mcp" \
  --test_depth="basic"
```

**Scoring Applications**:
- Protocol compliance scores
- Connectivity reliability ratings
- Communication quality metrics
- Interoperability assessments

### 5. System Health and Monitoring Scoring Tools

#### monitor_system_health
**Purpose**: Score overall system health and operational effectiveness
**Usage Pattern**: Continuous health scoring and alerting

```bash
# System health scoring monitoring
use_mcp_tool safla monitor_system_health \
  --check_interval=30 \
  --alert_thresholds={"cpu": 80, "memory": 85, "disk": 90}

# Comprehensive health scoring
use_mcp_tool safla monitor_system_health \
  --check_interval=15 \
  --alert_thresholds={"cpu": 75, "memory": 80, "disk": 85, "network": 70}
```

**Scoring Applications**:
- Overall system health scores
- Component-level health ratings
- Operational efficiency metrics
- Availability and uptime scores

#### get_system_info
**Purpose**: Collect system information for baseline scoring
**Usage Pattern**: System capability and configuration scoring

```bash
# System information for scoring baseline
use_mcp_tool safla get_system_info

# System configuration scoring data
use_mcp_tool safla validate_installation
```

**Scoring Applications**:
- System capability scores
- Configuration optimization ratings
- Resource utilization assessments
- Compatibility and readiness scores

### 6. Meta-Cognitive and Self-Awareness Scoring Tools

#### get_system_awareness
**Purpose**: Score meta-cognitive capabilities and self-awareness levels
**Usage Pattern**: Cognitive effectiveness scoring

```bash
# Meta-cognitive scoring assessment
use_mcp_tool safla get_system_awareness

# Self-awareness capability scoring
use_mcp_tool safla analyze_system_introspection \
  --analysis_type="comprehensive" \
  --time_window_hours=168
```

**Scoring Applications**:
- Self-awareness level scores
- Introspection depth ratings
- Meta-cognitive effectiveness metrics
- Consciousness development scores

#### analyze_system_introspection
**Purpose**: Score introspective analysis capabilities and effectiveness
**Usage Pattern**: Self-reflection quality scoring

```bash
# Introspection quality scoring
use_mcp_tool safla analyze_system_introspection \
  --analysis_type="behavior" \
  --time_window_hours=72

# Comprehensive introspection scoring
use_mcp_tool safla analyze_system_introspection \
  --analysis_type="comprehensive" \
  --time_window_hours=168
```

**Scoring Applications**:
- Introspection accuracy scores
- Self-analysis depth ratings
- Behavioral understanding metrics
- Cognitive development assessments

## Advanced Scoring Workflows

### 1. Composite Scoring Workflows

#### Multi-Dimensional Performance Scoring
```bash
# Comprehensive performance scoring workflow
echo "Starting multi-dimensional performance scoring..."

# Performance bottleneck scoring
PERF_SCORE=$(use_mcp_tool safla analyze_performance_bottlenecks --duration_seconds=300 | jq '.performance_score')

# Memory efficiency scoring
MEM_SCORE=$(use_mcp_tool safla benchmark_memory_performance --test_duration=120 | jq '.efficiency_score')

# Vector operation scoring
VEC_SCORE=$(use_mcp_tool safla benchmark_vector_operations --vector_count=5000 | jq '.throughput_score')

# MCP communication scoring
MCP_SCORE=$(use_mcp_tool safla benchmark_mcp_throughput --request_count=1000 | jq '.latency_score')

# Calculate composite performance score
COMPOSITE_SCORE=$(echo "scale=2; ($PERF_SCORE * 0.3) + ($MEM_SCORE * 0.25) + ($VEC_SCORE * 0.25) + ($MCP_SCORE * 0.2)" | bc)

echo "Composite Performance Score: $COMPOSITE_SCORE/100"
```

#### Goal Achievement Portfolio Scoring
```bash
# Portfolio-level goal achievement scoring
echo "Calculating portfolio goal achievement scores..."

# Get all active goals
GOALS=$(use_mcp_tool safla list_goals --status_filter="active" --priority_filter="all")

# Score each goal individually
for goal_id in $(echo $GOALS | jq -r '.goals[].id'); do
    GOAL_SCORE=$(use_mcp_tool safla evaluate_goal_progress --goal_id="$goal_id" | jq '.achievement_score')
    GOAL_WEIGHT=$(echo $GOALS | jq -r ".goals[] | select(.id==\"$goal_id\") | .weight")
    
    echo "Goal $goal_id: Score=$GOAL_SCORE, Weight=$GOAL_WEIGHT"
done

# Calculate weighted portfolio score
PORTFOLIO_SCORE=$(echo $GOALS | jq '[.goals[] | .achievement_score * .weight] | add / ([.goals[].weight] | add)')
echo "Portfolio Achievement Score: $PORTFOLIO_SCORE/100"
```

### 2. Comparative Scoring Workflows

#### Strategy Effectiveness Comparison
```bash
# Compare multiple strategies for effectiveness scoring
echo "Comparing strategy effectiveness scores..."

STRATEGIES=("optimization_v1" "optimization_v2" "optimization_v3")

for strategy in "${STRATEGIES[@]}"; do
    EFFECTIVENESS=$(use_mcp_tool safla evaluate_strategy_performance \
        --strategy_id="$strategy" \
        --evaluation_period_hours=168 | jq '.effectiveness_score')
    
    echo "Strategy $strategy: Effectiveness Score = $EFFECTIVENESS/100"
done

# Select optimal strategy based on scoring
OPTIMAL=$(use_mcp_tool safla select_optimal_strategy \
    --context="performance_optimization" \
    --objectives=["speed","memory","reliability"] | jq -r '.selected_strategy')

echo "Optimal Strategy Selected: $OPTIMAL"
```

#### Quality Assessment Comparison
```bash
# Compare quality scores across different dimensions
echo "Performing comprehensive quality scoring comparison..."

# Integration test quality scoring
INTEGRATION_SCORE=$(use_mcp_tool safla run_integration_tests --parallel=true | jq '.quality_score')

# Memory operation quality scoring
MEMORY_SCORE=$(use_mcp_tool safla validate_memory_operations --test_data_size=50 | jq '.quality_score')

# MCP connectivity quality scoring
CONNECTIVITY_SCORE=$(use_mcp_tool safla test_mcp_connectivity --test_depth="comprehensive" | jq '.quality_score')

echo "Quality Scores:"
echo "  Integration: $INTEGRATION_SCORE/100"
echo "  Memory: $MEMORY_SCORE/100"
echo "  Connectivity: $CONNECTIVITY_SCORE/100"

# Calculate overall quality score
OVERALL_QUALITY=$(echo "scale=2; ($INTEGRATION_SCORE + $MEMORY_SCORE + $CONNECTIVITY_SCORE) / 3" | bc)
echo "Overall Quality Score: $OVERALL_QUALITY/100"
```

### 3. Trend Analysis and Predictive Scoring

#### Performance Trend Scoring
```bash
# Analyze performance trends for predictive scoring
echo "Analyzing performance trends for predictive scoring..."

# Collect historical performance data
CURRENT_PERF=$(use_mcp_tool safla analyze_performance_bottlenecks --duration_seconds=300 | jq '.performance_score')

# Analyze adaptation patterns for trend prediction
ADAPTATION_TREND=$(use_mcp_tool safla analyze_adaptation_patterns \
    --pattern_type="performance" \
    --analysis_depth="comprehensive" \
    --time_window_days=30 | jq '.trend_score')

# Calculate trend-adjusted performance score
TREND_ADJUSTED_SCORE=$(echo "scale=2; $CURRENT_PERF * (1 + $ADAPTATION_TREND/100)" | bc)

echo "Current Performance: $CURRENT_PERF/100"
echo "Adaptation Trend: $ADAPTATION_TREND%"
echo "Trend-Adjusted Score: $TREND_ADJUSTED_SCORE/100"
```

#### Learning Progress Scoring
```bash
# Score learning progress and predict future performance
echo "Scoring learning progress and future performance prediction..."

# Get current learning metrics
LEARNING_METRICS=$(use_mcp_tool safla get_learning_metrics --metric_type="all" --time_range_hours=168)

ACCURACY_SCORE=$(echo $LEARNING_METRICS | jq '.accuracy_score')
RETENTION_SCORE=$(echo $LEARNING_METRICS | jq '.retention_score')
ADAPTATION_RATE=$(echo $LEARNING_METRICS | jq '.adaptation_rate')

# Calculate learning effectiveness score
LEARNING_EFFECTIVENESS=$(echo "scale=2; ($ACCURACY_SCORE * 0.4) + ($RETENTION_SCORE * 0.3) + ($ADAPTATION_RATE * 0.3)" | bc)

echo "Learning Effectiveness Score: $LEARNING_EFFECTIVENESS/100"

# Predict future learning performance
PREDICTED_IMPROVEMENT=$(echo "scale=2; $LEARNING_EFFECTIVENESS * $ADAPTATION_RATE / 100" | bc)
echo "Predicted Performance Improvement: $PREDICTED_IMPROVEMENT%"
```

## Scoring Data Integration Patterns

### 1. Resource-Based Scoring Data Collection

#### Performance Metrics Integration
```bash
# Collect performance data from SAFLA resources for scoring
echo "Collecting performance metrics for comprehensive scoring..."

# Access real-time performance metrics
PERF_METRICS=$(access_mcp_resource safla safla://performance-metrics)

# Access benchmark results for comparison
BENCHMARK_DATA=$(access_mcp_resource safla safla://benchmark-results)

# Access performance baselines for scoring context
BASELINE_DATA=$(access_mcp_resource safla safla://performance-baselines)

# Calculate performance improvement scores
echo "Processing performance data for scoring calculations..."
```

#### Goal and Strategy Data Integration
```bash
# Integrate goal and strategy data for portfolio scoring
echo "Integrating goal and strategy data for comprehensive scoring..."

# Access current goals data
GOALS_DATA=$(access_mcp_resource safla safla://goals)

# Access strategy performance data
STRATEGY_DATA=$(access_mcp_resource safla safla://strategies)

# Access learning metrics for adaptive scoring
LEARNING_DATA=$(access_mcp_resource safla safla://learning-metrics)

echo "Goal and strategy data integrated for scoring analysis"
```

### 2. Quality and Testing Data Integration

#### Test Results Integration
```bash
# Integrate test results for quality scoring
echo "Integrating test results for quality scoring analysis..."

# Access test execution results
TEST_RESULTS=$(access_mcp_resource safla safla://test-results)

# Access test coverage data
COVERAGE_DATA=$(access_mcp_resource safla safla://test-coverage)

# Calculate quality scores based on test data
QUALITY_SCORE=$(echo $TEST_RESULTS | jq '.overall_quality_score')
COVERAGE_SCORE=$(echo $COVERAGE_DATA | jq '.coverage_percentage')

echo "Quality Score: $QUALITY_SCORE/100"
echo "Coverage Score: $COVERAGE_SCORE%"
```

#### Optimization Recommendations Integration
```bash
# Integrate optimization recommendations for improvement scoring
echo "Integrating optimization recommendations for scoring..."

# Access optimization recommendations
OPT_RECOMMENDATIONS=$(access_mcp_resource safla safla://optimization-recommendations)

# Score optimization potential
OPT_POTENTIAL=$(echo $OPT_RECOMMENDATIONS | jq '.optimization_potential_score')

echo "Optimization Potential Score: $OPT_POTENTIAL/100"
```

## Error Handling in Scoring Workflows

### 1. Data Quality Validation

#### Missing Data Handling
```bash
# Handle missing data in scoring workflows
score_with_validation() {
    local tool_name=$1
    local params=$2
    
    # Attempt to collect scoring data
    if RESULT=$(use_mcp_tool safla $tool_name $params 2>/dev/null); then
        echo $RESULT | jq '.score // 0'
    else
        echo "0"  # Default score for missing data
        echo "Warning: Unable to collect data for $tool_name" >&2
    fi
}

# Example usage with validation
PERF_SCORE=$(score_with_validation "analyze_performance_bottlenecks" "--duration_seconds=300")
```

#### Score Validation and Bounds Checking
```bash
# Validate scores and ensure proper bounds
validate_score() {
    local score=$1
    local min_score=${2:-0}
    local max_score=${3:-100}
    
    # Ensure score is within valid bounds
    if (( $(echo "$score < $min_score" | bc -l) )); then
        echo $min_score
    elif (( $(echo "$score > $max_score" | bc -l) )); then
        echo $max_score
    else
        echo $score
    fi
}

# Example usage
RAW_SCORE=105.5
VALIDATED_SCORE=$(validate_score $RAW_SCORE 0 100)
echo "Validated Score: $VALIDATED_SCORE"
```

### 2. Fallback Scoring Mechanisms

#### Baseline Score Fallback
```bash
# Implement fallback to baseline scores when primary scoring fails
get_score_with_fallback() {
    local primary_tool=$1
    local fallback_baseline=$2
    
    # Try primary scoring method
    if PRIMARY_SCORE=$(use_mcp_tool safla $primary_tool 2>/dev/null | jq '.score'); then
        echo $PRIMARY_SCORE
    else
        # Fall back to baseline score
        echo $fallback_baseline
        echo "Warning: Using fallback baseline score" >&2
    fi
}

# Example usage
PERFORMANCE_SCORE=$(get_score_with_fallback "analyze_performance_bottlenecks --duration_seconds=300" "75")
```

#### Historical Score Interpolation
```bash
# Use historical data for score interpolation when current data unavailable
interpolate_score_from_history() {
    local metric_type=$1
    local time_range=$2
    
    # Access historical data
    if HISTORICAL_DATA=$(access_mcp_resource safla safla://performance-metrics 2>/dev/null); then
        # Calculate interpolated score from historical trends
        echo $HISTORICAL_DATA | jq ".historical_scores[] | select(.metric==\"$metric_type\") | .scores[-3:] | add / length"
    else
        echo "50"  # Default neutral score
    fi
}
```

This comprehensive MCP tools documentation provides the Scorer mode with detailed guidance for leveraging SAFLA's capabilities to implement effective quantitative evaluation and scoring workflows within the aiGI framework.