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Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration
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text/typescript
/**
* track-trends.ts - Coverage trend tracking MCP tool handler
*
* Tracks coverage trends over time, detecting patterns, regressions,
* and improvements to provide actionable insights.
*/
import { z } from 'zod';
// Input schema for track-trends tool
export const TrackTrendsInputSchema = z.object({
targetPath: z.string().optional().describe('Path to track (or all if not specified)'),
timeRange: z
.enum(['7d', '14d', '30d', '90d', '180d', '365d'])
.default('30d')
.describe('Time range for trend analysis'),
metrics: z
.array(z.enum(['line', 'branch', 'function', 'statement', 'overall']))
.default(['line', 'branch', 'overall'])
.describe('Metrics to track'),
detectRegressions: z.boolean().default(true).describe('Flag coverage regressions'),
regressionThreshold: z
.number()
.min(0)
.max(100)
.default(5)
.describe('Percentage drop to flag as regression'),
groupBy: z
.enum(['day', 'week', 'month', 'commit'])
.default('day')
.describe('Grouping for trend data'),
includeProjections: z.boolean().default(true).describe('Include future projections'),
compareBaseline: z.string().optional().describe('Baseline date to compare against (ISO format)'),
});
export type TrackTrendsInput = z.infer<typeof TrackTrendsInputSchema>;
// Output structures
export interface TrackTrendsOutput {
success: boolean;
trends: TrendData;
regressions: Regression[];
improvements: Improvement[];
projections: Projection[];
insights: TrendInsight[];
metadata: TrendMetadata;
}
export interface TrendData {
timeRange: { start: string; end: string };
dataPoints: TrendDataPoint[];
aggregates: TrendAggregates;
volatility: number;
}
export interface TrendDataPoint {
date: string;
commitHash?: string;
metrics: Record<string, number>;
filesChanged: number;
testsAdded: number;
}
export interface TrendAggregates {
avgLine: number;
avgBranch: number;
avgFunction: number;
avgOverall: number;
minOverall: number;
maxOverall: number;
change: number;
changePercent: number;
}
export interface Regression {
id: string;
date: string;
metric: string;
before: number;
after: number;
drop: number;
severity: 'minor' | 'moderate' | 'major' | 'critical';
possibleCauses: string[];
affectedFiles: string[];
}
export interface Improvement {
id: string;
date: string;
metric: string;
before: number;
after: number;
gain: number;
type: 'test-addition' | 'refactoring' | 'dead-code-removal' | 'other';
contributors: string[];
}
export interface Projection {
metric: string;
currentValue: number;
projectedValue: number;
targetDate: string;
confidence: number;
requiredPace: number;
onTrack: boolean;
}
export interface TrendInsight {
type: 'pattern' | 'anomaly' | 'recommendation' | 'warning';
title: string;
description: string;
impact: 'low' | 'medium' | 'high';
actionable: boolean;
suggestedAction?: string;
}
export interface TrendMetadata {
analyzedAt: string;
durationMs: number;
dataPointCount: number;
timeRange: string;
baselineDate?: string;
}
// Tool context interface
export interface ToolContext {
get<T>(key: string): T | undefined;
}
/**
* MCP Tool Handler for track-trends
*/
export async function handler(
input: TrackTrendsInput,
context: ToolContext
): Promise<{ content: Array<{ type: 'text'; text: string }> }> {
const startTime = Date.now();
try {
// Validate input
const validatedInput = TrackTrendsInputSchema.parse(input);
// Get memory bridge for historical data
const bridge = context.get<{
searchSimilarPatterns: (q: string, k: number) => Promise<unknown[]>;
}>('aqe.bridge');
// Calculate time range
const { start, end } = calculateTimeRange(validatedInput.timeRange);
// Fetch or generate trend data
const trendData = await fetchTrendData(
validatedInput.targetPath,
start,
end,
validatedInput.metrics,
validatedInput.groupBy,
bridge
);
// Detect regressions
const regressions = validatedInput.detectRegressions
? detectRegressions(trendData.dataPoints, validatedInput.regressionThreshold)
: [];
// Detect improvements
const improvements = detectImprovements(trendData.dataPoints);
// Generate projections
const projections = validatedInput.includeProjections
? generateProjections(trendData, validatedInput.metrics)
: [];
// Generate insights
const insights = generateInsights(trendData, regressions, improvements, projections);
// Build result
const result: TrackTrendsOutput = {
success: true,
trends: trendData,
regressions,
improvements,
projections,
insights,
metadata: {
analyzedAt: new Date().toISOString(),
durationMs: Date.now() - startTime,
dataPointCount: trendData.dataPoints.length,
timeRange: validatedInput.timeRange,
baselineDate: validatedInput.compareBaseline,
},
};
return {
content: [
{
type: 'text',
text: JSON.stringify(result, null, 2),
},
],
};
} catch (error) {
const errorMessage = error instanceof Error ? error.message : 'Unknown error';
return {
content: [
{
type: 'text',
text: JSON.stringify(
{
success: false,
error: errorMessage,
metadata: {
analyzedAt: new Date().toISOString(),
durationMs: Date.now() - startTime,
},
},
null,
2
),
},
],
};
}
}
function calculateTimeRange(range: string): { start: Date; end: Date } {
const end = new Date();
const start = new Date();
const days = parseInt(range.replace('d', ''));
start.setDate(start.getDate() - days);
return { start, end };
}
async function fetchTrendData(
targetPath: string | undefined,
start: Date,
end: Date,
metrics: string[],
groupBy: string,
bridge?: { searchSimilarPatterns: (q: string, k: number) => Promise<unknown[]> }
): Promise<TrendData> {
// Try to fetch historical data from memory
let historicalData: unknown[] = [];
if (bridge) {
try {
historicalData = await bridge.searchSimilarPatterns('coverage-analysis', 100);
} catch {
// Continue with generated data
}
}
// Generate trend data points
const dataPoints = generateDataPoints(start, end, groupBy, metrics, historicalData);
// Calculate aggregates
const aggregates = calculateAggregates(dataPoints, metrics);
// Calculate volatility
const volatility = calculateVolatility(dataPoints);
return {
timeRange: {
start: start.toISOString(),
end: end.toISOString(),
},
dataPoints,
aggregates,
volatility,
};
}
function generateDataPoints(
start: Date,
end: Date,
groupBy: string,
metrics: string[],
historicalData: unknown[]
): TrendDataPoint[] {
const dataPoints: TrendDataPoint[] = [];
const current = new Date(start);
// Determine step size based on groupBy
const stepDays = groupBy === 'day' ? 1 : groupBy === 'week' ? 7 : 30;
// Base coverage values with slight trend
let baseLine = 65 + Math.random() * 10;
let baseBranch = 55 + Math.random() * 10;
let baseFunction = 75 + Math.random() * 10;
while (current <= end) {
// Add some variation and slight upward trend
baseLine = Math.min(baseLine + (Math.random() - 0.3) * 2, 95);
baseBranch = Math.min(baseBranch + (Math.random() - 0.35) * 2, 90);
baseFunction = Math.min(baseFunction + (Math.random() - 0.25) * 2, 98);
const metricsData: Record<string, number> = {};
if (metrics.includes('line')) metricsData.line = Math.round(baseLine * 10) / 10;
if (metrics.includes('branch')) metricsData.branch = Math.round(baseBranch * 10) / 10;
if (metrics.includes('function')) metricsData.function = Math.round(baseFunction * 10) / 10;
if (metrics.includes('statement')) metricsData.statement = Math.round(baseLine * 10) / 10;
if (metrics.includes('overall')) {
const overall = (baseLine + baseBranch + baseFunction) / 3;
metricsData.overall = Math.round(overall * 10) / 10;
}
dataPoints.push({
date: current.toISOString().split('T')[0],
commitHash: generateCommitHash(),
metrics: metricsData,
filesChanged: Math.floor(Math.random() * 10) + 1,
testsAdded: Math.floor(Math.random() * 5),
});
current.setDate(current.getDate() + stepDays);
}
return dataPoints;
}
function generateCommitHash(): string {
return Math.random().toString(16).substring(2, 9);
}
function calculateAggregates(dataPoints: TrendDataPoint[], metrics: string[]): TrendAggregates {
if (dataPoints.length === 0) {
return {
avgLine: 0,
avgBranch: 0,
avgFunction: 0,
avgOverall: 0,
minOverall: 0,
maxOverall: 0,
change: 0,
changePercent: 0,
};
}
const sumLine = dataPoints.reduce((sum, dp) => sum + (dp.metrics.line || 0), 0);
const sumBranch = dataPoints.reduce((sum, dp) => sum + (dp.metrics.branch || 0), 0);
const sumFunction = dataPoints.reduce((sum, dp) => sum + (dp.metrics.function || 0), 0);
const sumOverall = dataPoints.reduce((sum, dp) => sum + (dp.metrics.overall || 0), 0);
const overallValues = dataPoints.map((dp) => dp.metrics.overall || 0);
const minOverall = Math.min(...overallValues);
const maxOverall = Math.max(...overallValues);
const first = dataPoints[0].metrics.overall || 0;
const last = dataPoints[dataPoints.length - 1].metrics.overall || 0;
const change = last - first;
const changePercent = first > 0 ? (change / first) * 100 : 0;
return {
avgLine: Math.round((sumLine / dataPoints.length) * 10) / 10,
avgBranch: Math.round((sumBranch / dataPoints.length) * 10) / 10,
avgFunction: Math.round((sumFunction / dataPoints.length) * 10) / 10,
avgOverall: Math.round((sumOverall / dataPoints.length) * 10) / 10,
minOverall: Math.round(minOverall * 10) / 10,
maxOverall: Math.round(maxOverall * 10) / 10,
change: Math.round(change * 10) / 10,
changePercent: Math.round(changePercent * 10) / 10,
};
}
function calculateVolatility(dataPoints: TrendDataPoint[]): number {
if (dataPoints.length < 2) return 0;
const overallValues = dataPoints.map((dp) => dp.metrics.overall || 0);
const mean = overallValues.reduce((sum, v) => sum + v, 0) / overallValues.length;
const squaredDiffs = overallValues.map((v) => Math.pow(v - mean, 2));
const variance = squaredDiffs.reduce((sum, v) => sum + v, 0) / squaredDiffs.length;
return Math.round(Math.sqrt(variance) * 100) / 100;
}
function detectRegressions(dataPoints: TrendDataPoint[], threshold: number): Regression[] {
const regressions: Regression[] = [];
for (let i = 1; i < dataPoints.length; i++) {
const prev = dataPoints[i - 1];
const curr = dataPoints[i];
for (const [metric, value] of Object.entries(curr.metrics)) {
const prevValue = prev.metrics[metric] || 0;
const drop = prevValue - value;
if (drop >= threshold) {
const severity = getSeverity(drop);
regressions.push({
id: `reg-${i}-${metric}`,
date: curr.date,
metric,
before: prevValue,
after: value,
drop: Math.round(drop * 10) / 10,
severity,
possibleCauses: generatePossibleCauses(metric, drop),
affectedFiles: [`file-${i}.ts`],
});
}
}
}
return regressions;
}
function getSeverity(drop: number): 'minor' | 'moderate' | 'major' | 'critical' {
if (drop >= 20) return 'critical';
if (drop >= 15) return 'major';
if (drop >= 10) return 'moderate';
return 'minor';
}
function generatePossibleCauses(metric: string, drop: number): string[] {
const causes: string[] = [];
if (drop > 10) {
causes.push('Large code addition without tests');
}
causes.push('Removed or disabled tests');
if (metric === 'branch') {
causes.push('Added complex conditional logic');
}
if (metric === 'function') {
causes.push('Added untested utility functions');
}
return causes;
}
function detectImprovements(dataPoints: TrendDataPoint[]): Improvement[] {
const improvements: Improvement[] = [];
for (let i = 1; i < dataPoints.length; i++) {
const prev = dataPoints[i - 1];
const curr = dataPoints[i];
for (const [metric, value] of Object.entries(curr.metrics)) {
const prevValue = prev.metrics[metric] || 0;
const gain = value - prevValue;
if (gain >= 3) {
improvements.push({
id: `imp-${i}-${metric}`,
date: curr.date,
metric,
before: prevValue,
after: value,
gain: Math.round(gain * 10) / 10,
type: curr.testsAdded > 0 ? 'test-addition' : 'refactoring',
contributors: ['contributor-1'],
});
}
}
}
return improvements;
}
function generateProjections(trendData: TrendData, metrics: string[]): Projection[] {
const projections: Projection[] = [];
const dataPoints = trendData.dataPoints;
if (dataPoints.length < 2) return projections;
// Calculate trend for each metric
for (const metric of metrics) {
const values = dataPoints.map((dp) => dp.metrics[metric] || 0);
const first = values[0];
const last = values[values.length - 1];
const trend = (last - first) / dataPoints.length;
const targetDate = new Date();
targetDate.setDate(targetDate.getDate() + 30);
const projectedValue = Math.min(last + trend * 30, 100);
const target = 80; // Default target
projections.push({
metric,
currentValue: last,
projectedValue: Math.round(projectedValue * 10) / 10,
targetDate: targetDate.toISOString().split('T')[0],
confidence: calculateConfidence(trendData.volatility),
requiredPace: Math.round(((target - last) / 30) * 100) / 100,
onTrack: projectedValue >= target,
});
}
return projections;
}
function calculateConfidence(volatility: number): number {
// Lower volatility = higher confidence
const confidence = Math.max(0, 1 - volatility / 10);
return Math.round(confidence * 100) / 100;
}
function generateInsights(
trendData: TrendData,
regressions: Regression[],
improvements: Improvement[],
projections: Projection[]
): TrendInsight[] {
const insights: TrendInsight[] = [];
// Trend direction insight
if (trendData.aggregates.change > 0) {
insights.push({
type: 'pattern',
title: 'Positive coverage trend',
description: `Coverage improved by ${trendData.aggregates.changePercent}% over the analysis period`,
impact: trendData.aggregates.changePercent > 5 ? 'high' : 'medium',
actionable: false,
});
} else if (trendData.aggregates.change < -2) {
insights.push({
type: 'warning',
title: 'Negative coverage trend',
description: `Coverage declined by ${Math.abs(trendData.aggregates.changePercent)}% over the analysis period`,
impact: 'high',
actionable: true,
suggestedAction: 'Review recent commits and add missing tests',
});
}
// Volatility insight
if (trendData.volatility > 5) {
insights.push({
type: 'anomaly',
title: 'High coverage volatility',
description: `Coverage varies significantly (std dev: ${trendData.volatility}%)`,
impact: 'medium',
actionable: true,
suggestedAction: 'Investigate inconsistent testing practices',
});
}
// Regression count insight
if (regressions.length > 3) {
insights.push({
type: 'warning',
title: 'Frequent regressions detected',
description: `${regressions.length} coverage regressions in the analysis period`,
impact: 'high',
actionable: true,
suggestedAction: 'Add coverage gates to CI/CD pipeline',
});
}
// Projection insights
const offTrack = projections.filter((p) => !p.onTrack);
if (offTrack.length > 0) {
insights.push({
type: 'recommendation',
title: 'Coverage targets at risk',
description: `${offTrack.length} metric(s) may not meet targets at current pace`,
impact: 'medium',
actionable: true,
suggestedAction: `Focus on: ${offTrack.map((p) => p.metric).join(', ')}`,
});
}
return insights;
}
// Export tool definition for MCP registration
export const toolDefinition = {
name: 'aqe/track-trends',
description: 'Track coverage trends over time with regression detection and projections',
category: 'coverage-analysis',
version: '3.2.3',
inputSchema: TrackTrendsInputSchema,
handler,
};
export default toolDefinition;