mcp-context-engineering/dist/src/mongodb/models/projectContext.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';
// Project DNA Schema (Universal equivalent of Cole's CLAUDE.md)
export const ProjectDNASchema = z.object({
    project_id: z.string(),
    project_name: z.string(),
    // Coding standards and patterns (extracted from codebase)
    coding_standards: z.object({
        style_guide: z.string(),
        naming_conventions: z.object({
            variables: z.string(),
            functions: z.string(),
            classes: z.string(),
            files: z.string()
        }),
        code_organization: z.object({
            directory_structure: z.string(),
            module_patterns: z.array(z.string()),
            import_conventions: z.array(z.string())
        }),
        best_practices: z.array(z.string()),
        anti_patterns: z.array(z.string())
    }),
    // Technology stack and architecture
    tech_stack: z.object({
        languages: z.array(z.string()),
        frameworks: z.array(z.string()),
        libraries: z.array(z.string()),
        databases: z.array(z.string()),
        tools: z.array(z.string()),
        version_requirements: z.record(z.string())
    }),
    // Project-specific rules and preferences
    project_rules: z.object({
        development_guidelines: z.array(z.string()),
        testing_requirements: z.array(z.string()),
        documentation_standards: z.array(z.string()),
        deployment_patterns: z.array(z.string()),
        security_requirements: z.array(z.string())
    }),
    // Architecture patterns and decisions
    architecture: z.object({
        design_patterns: z.array(z.string()),
        architectural_decisions: z.array(z.object({
            decision: z.string(),
            rationale: z.string(),
            alternatives_considered: z.array(z.string()),
            consequences: z.array(z.string())
        })),
        integration_patterns: z.array(z.string()),
        data_flow_patterns: z.array(z.string())
    }),
    // Context Portal workspace data
    workspace_context: z.object({
        active_features: z.array(z.string()),
        current_priorities: z.array(z.string()),
        known_issues: z.array(z.string()),
        recent_changes: z.array(z.object({
            change: z.string(),
            date: z.date(),
            impact: z.string()
        }))
    })
});
// Agent-specific project preferences
export const AgentProjectPreferencesSchema = z.object({
    cursor: z.object({
        preferred_context_sections: z.array(z.string()),
        code_example_preferences: z.string(),
        explanation_depth: z.enum(['minimal', 'moderate', 'detailed']),
        focus_areas: z.array(z.string())
    }),
    windsurf: z.object({
        preferred_context_sections: z.array(z.string()),
        step_by_step_preference: z.boolean(),
        error_handling_emphasis: z.boolean(),
        explanation_depth: z.enum(['minimal', 'moderate', 'detailed']),
        focus_areas: z.array(z.string())
    }),
    claude_code: z.object({
        preferred_context_sections: z.array(z.string()),
        full_prp_preference: z.boolean(),
        comprehensive_context: z.boolean(),
        explanation_depth: z.enum(['minimal', 'moderate', 'detailed']),
        focus_areas: z.array(z.string())
    }),
    generic: z.object({
        preferred_context_sections: z.array(z.string()),
        balanced_approach: z.boolean(),
        explanation_depth: z.enum(['minimal', 'moderate', 'detailed']),
        focus_areas: z.array(z.string())
    })
});
// Project evolution tracking
export const ProjectEvolutionSchema = z.object({
    version: z.number(),
    evolution_history: z.array(z.object({
        version: z.number(),
        changes: z.object({
            standards_updates: z.array(z.string()),
            pattern_additions: z.array(z.string()),
            rule_modifications: z.array(z.string()),
            architecture_changes: z.array(z.string())
        }),
        reason: z.string(),
        timestamp: z.date(),
        effectiveness_impact: z.number().min(-1).max(1)
    })),
    learning_insights: z.array(z.object({
        insight: z.string(),
        source: z.enum(['successful_pattern', 'failed_pattern', 'user_feedback', 'cross_project']),
        confidence: z.number().min(0).max(1),
        applied_date: z.date()
    }))
});
// Main Project Context Schema
export const ProjectContextSchema = z.object({
    _id: z.instanceof(ObjectId).optional(),
    // Core project DNA
    project_dna: ProjectDNASchema,
    // Agent-specific preferences
    agent_preferences: AgentProjectPreferencesSchema,
    // Evolution and learning
    evolution: ProjectEvolutionSchema,
    // Embedding for project similarity
    embedding: z.array(z.number()),
    // Metadata
    metadata: z.object({
        created_at: z.date(),
        updated_at: z.date(),
        last_accessed: z.date(),
        access_count: z.number(),
        active: z.boolean()
    })
});