mcp-context-engineering/dist/src/mongodb/models/effectivenessMetrics.js

The intelligent context optimization system for AI coding assistants. Built with Cole's PRP methodology, Context Portal knowledge graphs, and production-ready MongoDB architecture.

import { ObjectId } from 'mongodb';
import { z } from 'zod';
// Individual effectiveness measurement
export const EffectivenessMeasurementSchema = z.object({
    measurement_id: z.string(),
    context_pattern_id: z.string(),
    agent_type: z.enum(['cursor', 'windsurf', 'claude_code', 'generic']),
    // Implementation results
    implementation_result: z.object({
        success: z.boolean(),
        quality_score: z.number().min(0).max(10),
        time_to_completion: z.number().optional(), // minutes
        iterations_required: z.number(),
        user_satisfaction: z.number().min(0).max(10).optional()
    }),
    // Context effectiveness
    context_effectiveness: z.object({
        relevance_score: z.number().min(0).max(10),
        completeness_score: z.number().min(0).max(10),
        clarity_score: z.number().min(0).max(10),
        actionability_score: z.number().min(0).max(10)
    }),
    // Specific feedback
    feedback: z.object({
        what_worked: z.array(z.string()),
        what_failed: z.array(z.string()),
        missing_information: z.array(z.string()),
        suggestions: z.array(z.string()),
        agent_specific_notes: z.string()
    }),
    // Context metadata
    context_metadata: z.object({
        feature_type: z.string(),
        complexity: z.enum(['low', 'medium', 'high']),
        project_type: z.string(),
        tech_stack: z.array(z.string())
    }),
    timestamp: z.date()
});
// Aggregated effectiveness analytics
export const EffectivenessAnalyticsSchema = z.object({
    pattern_id: z.string(),
    // Overall metrics
    overall: z.object({
        total_usages: z.number(),
        success_rate: z.number().min(0).max(1),
        average_quality_score: z.number().min(0).max(10),
        average_relevance: z.number().min(0).max(10),
        trend: z.enum(['improving', 'stable', 'declining'])
    }),
    // Agent-specific metrics
    by_agent: z.record(z.object({
        usage_count: z.number(),
        success_rate: z.number().min(0).max(1),
        average_quality_score: z.number().min(0).max(10),
        preferred_formats: z.array(z.string()),
        common_issues: z.array(z.string()),
        optimization_suggestions: z.array(z.string())
    })),
    // Project type metrics
    by_project_type: z.record(z.object({
        usage_count: z.number(),
        success_rate: z.number().min(0).max(1),
        average_quality_score: z.number().min(0).max(10),
        specific_adaptations: z.array(z.string())
    })),
    // Complexity metrics
    by_complexity: z.record(z.object({
        usage_count: z.number(),
        success_rate: z.number().min(0).max(1),
        average_completion_time: z.number().optional(),
        common_challenges: z.array(z.string())
    })),
    // Learning insights
    insights: z.object({
        top_success_factors: z.array(z.string()),
        common_failure_points: z.array(z.string()),
        optimization_opportunities: z.array(z.string()),
        cross_agent_learnings: z.array(z.string())
    }),
    last_updated: z.date()
});
// Cross-agent learning patterns
export const CrossAgentLearningSchema = z.object({
    learning_id: z.string(),
    // Source of learning
    source: z.object({
        agent_type: z.string(),
        pattern_id: z.string(),
        success_factor: z.string(),
        context: z.string()
    }),
    // Applicability analysis
    applicability: z.object({
        tested_agents: z.array(z.string()),
        success_rates: z.record(z.number()),
        adaptation_requirements: z.record(z.array(z.string())),
        limitations: z.array(z.string())
    }),
    // Implementation guidance
    implementation: z.object({
        universal_principle: z.string(),
        agent_specific_adaptations: z.record(z.string()),
        validation_criteria: z.array(z.string()),
        expected_impact: z.number().min(0).max(1)
    }),
    // Validation results
    validation: z.object({
        tested: z.boolean(),
        validation_results: z.array(z.object({
            agent_type: z.string(),
            before_score: z.number(),
            after_score: z.number(),
            improvement: z.number(),
            confirmed: z.boolean()
        })),
        deployment_ready: z.boolean()
    }),
    created_at: z.date(),
    updated_at: z.date()
});
// Main effectiveness tracking schema
export const EffectivenessTrackingSchema = z.object({
    _id: z.instanceof(ObjectId).optional(),
    // Individual measurements
    measurements: z.array(EffectivenessMeasurementSchema),
    // Aggregated analytics
    analytics: EffectivenessAnalyticsSchema,
    // Cross-agent learning
    cross_agent_learning: z.array(CrossAgentLearningSchema),
    // System-wide insights
    system_insights: z.object({
        most_effective_patterns: z.array(z.string()),
        agent_strengths: z.record(z.array(z.string())),
        universal_success_factors: z.array(z.string()),
        improvement_priorities: z.array(z.string())
    }),
    metadata: z.object({
        last_analysis: z.date(),
        next_analysis_due: z.date(),
        analysis_version: z.number()
    })
});