mcp-context-engineering
Version:
The intelligent context optimization system for AI coding assistants. Built with Cole's PRP methodology, Context Portal knowledge graphs, and production-ready MongoDB architecture.
219 lines (218 loc) • 7.74 kB
JavaScript
import { ObjectId } from 'mongodb';
import { z } from 'zod';
/**
* Context History Schema - Following Context Portal's versioning patterns
*
* Provides comprehensive context evolution tracking with:
* - Automatic versioning for all context changes
* - Change source tracking (user, system, AI agent)
* - Temporal queries for context at any point in time
* - Incremental version management
*/
// Individual context history entry
export const ContextHistoryEntrySchema = z.object({
_id: z.instanceof(ObjectId).optional(),
// Context identification
workspace_id: z.string(),
project_id: z.string(),
context_type: z.enum(['product_context', 'active_context', 'decision', 'progress_entry', 'system_pattern', 'context_pattern']),
context_item_id: z.string(),
// Version management
version: z.number(),
previous_version: z.number().optional(),
// Change tracking
change_source: z.enum(['user', 'system', 'ai_agent', 'api', 'automation']),
change_type: z.enum(['create', 'update', 'delete', 'archive', 'restore']),
change_summary: z.string(),
change_details: z.record(z.any()).optional(),
// Content snapshots
content_before: z.record(z.any()).optional(),
content_after: z.record(z.any()),
content_diff: z.record(z.any()).optional(),
// Metadata
timestamp: z.date(),
user_id: z.string().optional(),
agent_type: z.enum(['cursor', 'windsurf', 'claude_code', 'generic']).optional(),
session_id: z.string().optional(),
// Impact tracking
impact_score: z.number().min(0).max(1).optional(),
related_changes: z.array(z.string()).optional()
});
// Workspace evolution summary
export const WorkspaceEvolutionSchema = z.object({
_id: z.instanceof(ObjectId).optional(),
workspace_id: z.string(),
project_id: z.string(),
// Version statistics
current_version: z.number(),
total_changes: z.number(),
change_velocity: z.number(), // Changes per day
// Change patterns
change_frequency: z.object({
hourly: z.array(z.number()),
daily: z.array(z.number()),
weekly: z.array(z.number())
}),
// Source analytics
change_sources: z.record(z.number()),
agent_activity: z.record(z.number()),
// Content evolution
context_growth: z.object({
decisions_added: z.number(),
patterns_created: z.number(),
progress_entries: z.number(),
total_content_size: z.number()
}),
// Quality metrics
stability_score: z.number().min(0).max(1),
consistency_score: z.number().min(0).max(1),
completeness_score: z.number().min(0).max(1),
// Time tracking
created_at: z.date(),
last_updated: z.date(),
analysis_date: z.date()
});
// Context snapshot for temporal queries
export const ContextSnapshotSchema = z.object({
_id: z.instanceof(ObjectId).optional(),
// Snapshot identification
workspace_id: z.string(),
project_id: z.string(),
snapshot_version: z.number(),
snapshot_timestamp: z.date(),
// Complete context state at this version
context_state: z.object({
product_context: z.record(z.any()),
active_context: z.record(z.any()),
decisions: z.array(z.record(z.any())),
progress_entries: z.array(z.record(z.any())),
system_patterns: z.array(z.record(z.any())),
context_links: z.array(z.record(z.any()))
}),
// Snapshot metadata
created_by: z.enum(['system', 'user', 'ai_agent', 'scheduled']),
snapshot_reason: z.string(),
retention_policy: z.enum(['permanent', 'long_term', 'short_term']),
// Compression and optimization
compressed: z.boolean().default(false),
compression_ratio: z.number().optional(),
original_size: z.number().optional()
});
// Change analytics for learning and optimization
export const ChangeAnalyticsSchema = z.object({
_id: z.instanceof(ObjectId).optional(),
workspace_id: z.string(),
analysis_period: z.object({
start_date: z.date(),
end_date: z.date()
}),
// Change patterns
patterns: z.object({
most_active_hours: z.array(z.number()),
peak_activity_days: z.array(z.string()),
common_change_types: z.array(z.object({
type: z.string(),
frequency: z.number(),
impact: z.number()
}))
}),
// Agent behavior analytics
agent_behavior: z.record(z.object({
total_changes: z.number(),
avg_change_impact: z.number(),
preferred_change_types: z.array(z.string()),
effectiveness_score: z.number(),
learning_velocity: z.number()
})),
// Context health metrics
health_metrics: z.object({
consistency_trend: z.array(z.number()),
completeness_trend: z.array(z.number()),
stability_trend: z.array(z.number()),
growth_rate: z.number(),
quality_score: z.number()
}),
// Recommendations
recommendations: z.array(z.object({
category: z.enum(['optimization', 'maintenance', 'expansion', 'cleanup']),
priority: z.enum(['high', 'medium', 'low']),
description: z.string(),
expected_impact: z.number(),
effort_estimate: z.string()
})),
// Metadata
analysis_date: z.date(),
next_analysis_due: z.date(),
analysis_version: z.number()
});
// Main Context History Collection Schema
export const ContextHistorySchema = z.object({
_id: z.instanceof(ObjectId).optional(),
workspace_id: z.string(),
project_id: z.string(),
// History entries
history_entries: z.array(ContextHistoryEntrySchema),
// Evolution tracking
workspace_evolution: WorkspaceEvolutionSchema,
// Periodic snapshots
snapshots: z.array(ContextSnapshotSchema),
// Analytics and insights
change_analytics: ChangeAnalyticsSchema,
// Metadata
metadata: z.object({
created_at: z.date(),
last_updated: z.date(),
retention_policy: z.enum(['standard', 'extended', 'permanent']),
archive_after_days: z.number().default(365),
compress_after_days: z.number().default(90)
})
});
// Helper functions for version management
export class ContextVersionManager {
/**
* Generate next version number for a context item
*/
static generateNextVersion(currentVersion = 0) {
return currentVersion + 1;
}
/**
* Create change diff between two context states
*/
static createChangeDiff(before, after) {
const diff = {};
// Simple diff implementation - would be enhanced with proper diff library
for (const key in after) {
if (JSON.stringify(before?.[key]) !== JSON.stringify(after[key])) {
diff[key] = {
before: before?.[key],
after: after[key]
};
}
}
return diff;
}
/**
* Calculate impact score based on change magnitude
*/
static calculateImpactScore(changeDiff) {
const changeCount = Object.keys(changeDiff).length;
const maxChanges = 20; // Configurable threshold
return Math.min(changeCount / maxChanges, 1.0);
}
/**
* Create context snapshot for temporal queries
*/
static createSnapshot(workspaceId, projectId, version, contextState, reason = 'periodic') {
return {
workspace_id: workspaceId,
project_id: projectId,
snapshot_version: version,
snapshot_timestamp: new Date(),
context_state: contextState,
created_by: 'system',
snapshot_reason: reason,
retention_policy: 'long_term',
compressed: false
};
}
}