@ooples/token-optimizer-mcp
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Intelligent context window optimization for Claude Code - store content externally via caching and compression, freeing up your context window for what matters
898 lines • 39.3 kB
JavaScript
import { generateCacheKey } from '../shared/hash-utils.js';
import { createHash } from 'crypto';
// ============================================================================
// In-Memory Storage (Production: use database)
// ============================================================================
class PerformanceMetricStore {
metrics = new Map(); // metricId -> array of metrics
baselines = new Map();
maxMetricEntries = 100000; // Max entries per metricId
addMetric(metric) {
if (!this.metrics.has(metric.id)) {
this.metrics.set(metric.id, []);
}
const metricHistory = this.metrics.get(metric.id);
metricHistory.push(metric);
// Trim old history
if (metricHistory.length > this.maxMetricEntries) {
this.metrics.set(metric.id, metricHistory.slice(-this.maxMetricEntries));
}
}
getMetrics(metricId, timeRange, limit, tags) {
let filteredMetrics = [];
if (metricId) {
filteredMetrics = this.metrics.get(metricId) || [];
}
else {
// If no specific metricId, return all metrics (potentially very large)
// For simplicity, this implementation will only return if metricId is specified
// or if a specific tag filter is applied across all metrics (not implemented here)
return [];
}
if (timeRange) {
filteredMetrics = filteredMetrics.filter((m) => m.timestamp >= timeRange.start && m.timestamp <= timeRange.end);
}
if (tags) {
filteredMetrics = filteredMetrics.filter((m) => Object.entries(tags).every(([key, value]) => m.tags[key] === value));
}
// Sort by timestamp ascending
filteredMetrics.sort((a, b) => a.timestamp - b.timestamp);
if (limit) {
filteredMetrics = filteredMetrics.slice(-limit);
}
return filteredMetrics;
}
saveBaseline(baseline) {
this.baselines.set(baseline.id, baseline);
}
getBaseline(id) {
return this.baselines.get(id);
}
}
const performanceMetricStore = new PerformanceMetricStore();
// ============================================================================
// Statistical Engine
// ============================================================================
class StatisticalEngine {
// Simple linear regression
calculateLinearRegression(data) {
if (data.length < 2) {
return { slope: 0, intercept: 0, rSquared: 0 };
}
const n = data.length;
let sumX = 0;
let sumY = 0;
let sumXY = 0;
let sumXX = 0;
let sumYY = 0;
for (const point of data) {
sumX += point.x;
sumY += point.y;
sumXY += point.x * point.y;
sumXX += point.x * point.x;
sumYY += point.y * point.y;
}
const denominator = n * sumXX - sumX * sumX;
if (denominator === 0) {
return { slope: 0, intercept: sumY / n, rSquared: 0 }; // Vertical line or constant
}
const slope = (n * sumXY - sumX * sumY) / denominator;
const intercept = (sumY - slope * sumX) / n;
// Calculate R-squared
let ssTotal = 0;
let ssResidual = 0;
const meanY = sumY / n;
for (const point of data) {
ssTotal += (point.y - meanY) * (point.y - meanY);
const predictedY = slope * point.x + intercept;
ssResidual += (point.y - predictedY) * (point.y - predictedY);
}
const rSquared = ssTotal === 0 ? 1 : 1 - ssResidual / ssTotal; // If all y values are the same, R-squared is 1
return { slope, intercept, rSquared };
}
analyzeTrend(metrics) {
if (metrics.length < 2) {
return {
metricId: metrics[0]?.id || 'unknown',
trendType: 'unknown',
analysisPeriod: { start: 0, end: 0 },
recommendations: ['Not enough data to analyze trend.'],
};
}
const data = metrics.map((m) => ({ x: m.timestamp, y: m.value }));
const { slope, rSquared } = this.calculateLinearRegression(data);
let trendType = 'unknown';
const recommendations = [];
if (rSquared > 0.7) {
// Strong correlation
if (slope > 0.001) {
trendType = 'increasing';
recommendations.push('Performance is showing a strong increasing trend. Investigate potential resource bottlenecks or increased load.');
}
else if (slope < -0.001) {
trendType = 'decreasing';
recommendations.push('Performance is showing a strong decreasing trend. This is generally positive, but ensure it aligns with expectations (e.g., optimizations).');
}
else {
trendType = 'stable';
recommendations.push('Performance is stable. Continue monitoring.');
}
}
else if (rSquared > 0.3) {
// Moderate correlation
if (slope > 0.001) {
trendType = 'increasing';
recommendations.push('Performance is showing a moderate increasing trend. Keep an eye on it for potential issues.');
}
else if (slope < -0.001) {
trendType = 'decreasing';
recommendations.push('Performance is showing a moderate decreasing trend. Good, but verify the cause.');
}
else {
trendType = 'stable';
recommendations.push('Performance is relatively stable, but with some fluctuations. Consider reducing noise or improving measurement precision.');
}
}
else {
trendType = 'volatile';
recommendations.push('Performance is volatile or shows no clear trend. This could indicate inconsistent behavior or external factors. Investigate.');
}
return {
metricId: metrics[0].id,
trendType,
slope,
rSquared,
analysisPeriod: {
start: metrics[0].timestamp,
end: metrics[metrics.length - 1].timestamp,
},
recommendations,
};
}
forecast(metrics, horizon) {
if (metrics.length < 2) {
return {
metricId: metrics[0]?.id || 'unknown',
forecastPoints: [],
modelUsed: 'linear-regression',
confidenceInterval: 0,
};
}
const data = metrics.map((m) => ({ x: m.timestamp, y: m.value }));
const { slope, intercept } = this.calculateLinearRegression(data);
const forecastPoints = [];
const lastTimestamp = metrics[metrics.length - 1].timestamp;
const timeStep = (metrics[metrics.length - 1].timestamp - metrics[0].timestamp) /
(metrics.length - 1); // Average time difference between points
for (let i = 1; i <= horizon; i++) {
const futureTimestamp = lastTimestamp + i * timeStep;
const forecastedValue = slope * futureTimestamp + intercept;
forecastPoints.push({
timestamp: futureTimestamp,
value: forecastedValue,
});
}
return {
metricId: metrics[0].id,
forecastPoints,
modelUsed: 'linear-regression',
confidenceInterval: 0.95, // Placeholder
};
}
compare(metrics1, metrics2) {
if (metrics1.length === 0 || metrics2.length === 0) {
return {
metricId1: metrics1[0]?.id || 'unknown1',
metricId2: metrics2[0]?.id || 'unknown2',
comparisonResult: 'inconclusive',
details: 'Not enough data for comparison.',
};
}
const avg1 = metrics1.reduce((sum, m) => sum + m.value, 0) / metrics1.length;
const avg2 = metrics2.reduce((sum, m) => sum + m.value, 0) / metrics2.length;
let comparisonResult = 'similar';
let percentageChange;
let details = '';
if (avg1 === 0) {
percentageChange = avg2 === 0 ? 0 : 100; // If avg1 is 0, and avg2 is not, it's a 100% change
}
else {
percentageChange = ((avg2 - avg1) / avg1) * 100;
}
// Assuming lower value is better for most performance metrics (e.g., response time, CPU)
// This might need to be configurable based on metricType (e.g., throughput: higher is better)
const isLowerBetter = true; // Simplification
if (Math.abs(percentageChange) < 5) {
comparisonResult = 'similar';
details = `The two metrics are similar, with a difference of ${percentageChange.toFixed(2)}%.`;
}
else if (percentageChange > 0) {
comparisonResult = isLowerBetter ? 'worse' : 'better';
details = `Metric 2 is ${percentageChange.toFixed(2)}% ${isLowerBetter ? 'worse' : 'better'} than Metric 1.`;
}
else {
comparisonResult = isLowerBetter ? 'better' : 'worse';
details = `Metric 2 is ${Math.abs(percentageChange).toFixed(2)}% ${isLowerBetter ? 'better' : 'worse'} than Metric 1.`;
}
return {
metricId1: metrics1[0].id,
metricId2: metrics2[0].id,
comparisonResult,
percentageChange,
details,
};
}
detectRegression(metrics, threshold) {
if (metrics.length < 10) {
return {
metricId: metrics[0]?.id || 'unknown',
regressionDetected: false,
recommendations: ['Not enough data to detect regression.'],
};
}
// Simple regression detection: compare last N points average with previous N points average
const lookbackPeriod = Math.floor(metrics.length / 2);
const recentMetrics = metrics.slice(-lookbackPeriod);
const historicalMetrics = metrics.slice(0, lookbackPeriod);
if (recentMetrics.length === 0 || historicalMetrics.length === 0) {
return {
metricId: metrics[0]?.id || 'unknown',
regressionDetected: false,
recommendations: ['Not enough data to detect regression.'],
};
}
const recentAvg = recentMetrics.reduce((sum, m) => sum + m.value, 0) / recentMetrics.length;
const historicalAvg = historicalMetrics.reduce((sum, m) => sum + m.value, 0) /
historicalMetrics.length;
let regressionDetected = false;
let changePoint;
let thresholdExceeded;
const recommendations = [];
if (historicalAvg === 0) {
if (recentAvg > 0 && threshold < 100) {
// If historical was 0 and recent is not, and threshold is not 100%
regressionDetected = true;
thresholdExceeded = 100;
changePoint = recentMetrics[0].timestamp;
recommendations.push('Significant performance change detected from zero baseline. Investigate immediately.');
}
}
else {
const percentageChange = ((recentAvg - historicalAvg) / historicalAvg) * 100;
// Assuming higher value is worse for regression (e.g., higher response time, higher CPU)
const isHigherWorse = true; // Simplification
if (isHigherWorse && percentageChange > threshold) {
regressionDetected = true;
changePoint = recentMetrics[0].timestamp;
thresholdExceeded = percentageChange;
recommendations.push(`Performance regression detected! Metric value increased by ${percentageChange.toFixed(2)}% (threshold: ${threshold}%). Investigate recent changes.`);
}
else if (!isHigherWorse && percentageChange < -threshold) {
// If lower is worse, and value decreased significantly
regressionDetected = true;
changePoint = recentMetrics[0].timestamp;
thresholdExceeded = percentageChange;
recommendations.push(`Performance regression detected! Metric value decreased by ${Math.abs(percentageChange).toFixed(2)}% (threshold: ${threshold}%). Investigate recent changes.`);
}
}
if (!regressionDetected) {
recommendations.push('No significant performance regression detected.');
}
return {
metricId: metrics[0].id,
regressionDetected,
changePoint,
oldValue: historicalAvg,
newValue: recentAvg,
thresholdExceeded,
recommendations,
};
}
}
const statisticalEngine = new StatisticalEngine();
// ============================================================================
// Main PerformanceTracker Class
// ============================================================================
export class PerformanceTracker {
cache;
tokenCounter;
metricsCollector;
constructor(cache, tokenCounter, metricsCollector) {
this.cache = cache;
this.tokenCounter = tokenCounter;
this.metricsCollector = metricsCollector;
}
async run(options) {
const startTime = Date.now();
try {
// Validate operation
if (!options.operation) {
throw new Error('Operation is required');
}
// Execute operation
let result;
switch (options.operation) {
case 'track':
result = await this.trackMetric(options, startTime);
break;
case 'query':
result = await this.queryMetrics(options, startTime);
break;
case 'analyze-trends':
result = await this.analyzeTrends(options, startTime);
break;
case 'forecast':
result = await this.forecastPerformance(options, startTime);
break;
case 'compare':
result = await this.comparePerformance(options, startTime);
break;
case 'detect-regressions':
result = await this.detectRegressions(options, startTime);
break;
case 'get-baseline':
result = await this.getBaseline(options, startTime);
break;
case 'generate-report':
result = await this.generateReport(options, startTime);
break;
default:
throw new Error(`Unknown operation: ${options.operation}`);
}
// Record metrics
this.metricsCollector.record({
operation: `performance-tracker:${options.operation}`,
duration: Date.now() - startTime,
success: result.success,
cacheHit: result.metadata.cacheHit,
});
return result;
}
catch (error) {
// Record error metrics
this.metricsCollector.record({
operation: `performance-tracker:${options.operation}`,
duration: Date.now() - startTime,
success: false,
cacheHit: false,
});
return {
success: false,
error: error instanceof Error ? error.message : 'Unknown error',
metadata: {
cacheHit: false,
},
};
}
}
// ========================================================================
// Operation: track
// ========================================================================
async trackMetric(options, _startTime) {
if (!options.metricName ||
!options.metricType ||
options.value === undefined) {
throw new Error('metricName, metricType, and value are required for tracking');
}
const metricId = this.generateMetricId(options.metricName, options.metricType, options.tags || {});
const metric = {
id: metricId,
name: options.metricName,
type: options.metricType,
value: options.value,
timestamp: Date.now(),
tags: options.tags || {},
};
performanceMetricStore.addMetric(metric);
return {
success: true,
data: { metric },
metadata: {
cacheHit: false,
metricsTracked: 1,
},
};
}
// ========================================================================
// Operation: query
// ========================================================================
async queryMetrics(options, _startTime) {
if (!options.metricId && !options.metricName) {
throw new Error('metricId or metricName is required for querying');
}
const queryMetricId = options.metricId ||
this.generateMetricId(options.metricName, options.metricType || 'custom', options.tags || {});
// Generate cache key
const cacheKey = generateCacheKey('performance-query', {
metricId: queryMetricId,
timeRange: options.timeRange,
limit: options.limit,
tags: options.tags,
});
// Check cache (30-second TTL)
if (options.useCache !== false) {
const cached = this.cache.get(cacheKey);
if (cached) {
const data = JSON.parse(cached.toString());
const tokensSaved = this.tokenCounter.count(JSON.stringify(data)).tokens;
return {
success: true,
data: { metrics: data },
metadata: {
tokensSaved,
cacheHit: true,
metricsQueried: data.length,
},
};
}
}
const metrics = performanceMetricStore.getMetrics(queryMetricId, options.timeRange, options.limit, options.tags);
// Cache result
const metricsStr = JSON.stringify(metrics);
const tokensUsed = this.tokenCounter.count(metricsStr).tokens;
this.cache.set(cacheKey, Buffer.from(metricsStr).toString('utf-8'), metricsStr.length, Buffer.from(metricsStr).length);
return {
success: true,
data: { metrics },
metadata: {
tokensUsed,
cacheHit: false,
metricsQueried: metrics.length,
},
};
}
// ========================================================================
// Operation: analyze-trends
// ========================================================================
async analyzeTrends(options, _startTime) {
if (!options.metricId && !options.metricName) {
throw new Error('metricId or metricName is required for trend analysis');
}
const analyzeMetricId = options.metricId ||
this.generateMetricId(options.metricName, options.metricType || 'custom', options.tags || {});
// Generate cache key
const cacheKey = generateCacheKey('performance-trend', {
metricId: analyzeMetricId,
analysisPeriod: options.analysisPeriod,
tags: options.tags,
});
// Check cache (5-minute TTL)
if (options.useCache !== false) {
const cached = this.cache.get(cacheKey);
if (cached) {
const data = JSON.parse(cached.toString());
const tokensSaved = this.tokenCounter.count(JSON.stringify(data)).tokens;
return {
success: true,
data: { trend: data },
metadata: {
tokensSaved,
cacheHit: true,
},
};
}
}
const metrics = performanceMetricStore.getMetrics(analyzeMetricId, options.analysisPeriod, undefined, // No limit for trend analysis
options.tags);
if (metrics.length === 0) {
throw new Error('No metrics found for trend analysis');
}
const trend = statisticalEngine.analyzeTrend(metrics);
// Cache result
const trendStr = JSON.stringify(trend);
const tokensUsed = this.tokenCounter.count(trendStr).tokens;
this.cache.set(cacheKey, Buffer.from(trendStr).toString('utf-8'), trendStr.length, Buffer.from(trendStr).length);
return {
success: true,
data: { trend },
metadata: {
tokensUsed,
cacheHit: false,
},
};
}
// ========================================================================
// Operation: forecast
// ========================================================================
async forecastPerformance(options, _startTime) {
if (!options.metricId && !options.metricName) {
throw new Error('metricId or metricName is required for forecasting');
}
if (options.forecastHorizon === undefined || options.forecastHorizon <= 0) {
throw new Error('forecastHorizon (positive number) is required');
}
const forecastMetricId = options.metricId ||
this.generateMetricId(options.metricName, options.metricType || 'custom', options.tags || {});
// Generate cache key
const cacheKey = generateCacheKey('performance-forecast', {
metricId: forecastMetricId,
forecastHorizon: options.forecastHorizon,
tags: options.tags,
});
// Check cache (10-minute TTL)
if (options.useCache !== false) {
const cached = this.cache.get(cacheKey);
if (cached) {
const data = JSON.parse(cached.toString());
const tokensSaved = this.tokenCounter.count(JSON.stringify(data)).tokens;
return {
success: true,
data: { forecast: data },
metadata: {
tokensSaved,
cacheHit: true,
},
};
}
}
const metrics = performanceMetricStore.getMetrics(forecastMetricId, options.timeRange, undefined, // No limit for forecasting source data
options.tags);
if (metrics.length < 2) {
throw new Error('Not enough historical data to generate a forecast (at least 2 points needed)');
}
const forecast = statisticalEngine.forecast(metrics, options.forecastHorizon);
// Cache result
const forecastStr = JSON.stringify(forecast);
const tokensUsed = this.tokenCounter.count(forecastStr).tokens;
this.cache.set(cacheKey, Buffer.from(forecastStr).toString('utf-8'), forecastStr.length, Buffer.from(forecastStr).length);
return {
success: true,
data: { forecast },
metadata: {
tokensUsed,
cacheHit: false,
},
};
}
// ========================================================================
// Operation: compare
// ========================================================================
async comparePerformance(options, _startTime) {
if (!options.comparisonMetricId1 || !options.comparisonMetricId2) {
throw new Error('comparisonMetricId1 and comparisonMetricId2 are required for comparison');
}
// Generate cache key
const cacheKey = generateCacheKey('performance-compare', {
metricId1: options.comparisonMetricId1,
metricId2: options.comparisonMetricId2,
timeRange: options.timeRange,
tags: options.tags,
});
// Check cache (1-minute TTL)
if (options.useCache !== false) {
const cached = this.cache.get(cacheKey);
if (cached) {
const data = JSON.parse(cached.toString());
const tokensSaved = this.tokenCounter.count(JSON.stringify(data)).tokens;
return {
success: true,
data: { comparison: data },
metadata: {
tokensSaved,
cacheHit: true,
},
};
}
}
const metrics1 = performanceMetricStore.getMetrics(options.comparisonMetricId1, options.timeRange, undefined, options.tags);
const metrics2 = performanceMetricStore.getMetrics(options.comparisonMetricId2, options.timeRange, undefined, options.tags);
if (metrics1.length === 0 || metrics2.length === 0) {
throw new Error('Not enough data for one or both metrics for comparison');
}
const comparison = statisticalEngine.compare(metrics1, metrics2);
// Cache result
const comparisonStr = JSON.stringify(comparison);
const tokensUsed = this.tokenCounter.count(comparisonStr).tokens;
this.cache.set(cacheKey, Buffer.from(comparisonStr).toString('utf-8'), comparisonStr.length, Buffer.from(comparisonStr).length);
return {
success: true,
data: { comparison },
metadata: {
tokensUsed,
cacheHit: false,
},
};
}
// ========================================================================
// Operation: detect-regressions
// ========================================================================
async detectRegressions(options, _startTime) {
if (!options.metricId && !options.metricName) {
throw new Error('metricId or metricName is required for regression detection');
}
if (options.regressionThreshold === undefined ||
options.regressionThreshold <= 0) {
throw new Error('regressionThreshold (positive number) is required');
}
const regressionMetricId = options.metricId ||
this.generateMetricId(options.metricName, options.metricType || 'custom', options.tags || {});
// Generate cache key
const cacheKey = generateCacheKey('performance-regression', {
metricId: regressionMetricId,
timeRange: options.timeRange,
regressionThreshold: options.regressionThreshold,
tags: options.tags,
});
// Check cache (5-minute TTL)
if (options.useCache !== false) {
const cached = this.cache.get(cacheKey);
if (cached) {
const data = JSON.parse(cached.toString());
const tokensSaved = this.tokenCounter.count(JSON.stringify(data)).tokens;
return {
success: true,
data: { regression: data },
metadata: {
tokensSaved,
cacheHit: true,
},
};
}
}
const metrics = performanceMetricStore.getMetrics(regressionMetricId, options.timeRange, undefined, options.tags);
if (metrics.length < 10) {
throw new Error('Not enough data to detect regressions (at least 10 points recommended)');
}
const regression = statisticalEngine.detectRegression(metrics, options.regressionThreshold);
// Cache result
const regressionStr = JSON.stringify(regression);
const tokensUsed = this.tokenCounter.count(regressionStr).tokens;
this.cache.set(cacheKey, Buffer.from(regressionStr).toString('utf-8'), regressionStr.length, Buffer.from(regressionStr).length);
return {
success: true,
data: { regression },
metadata: {
tokensUsed,
cacheHit: false,
},
};
}
// ========================================================================
// Operation: get-baseline
// ========================================================================
async getBaseline(options, _startTime) {
if (!options.baselineId) {
throw new Error('baselineId is required to get a baseline');
}
// Generate cache key
const cacheKey = generateCacheKey('performance-baseline', {
baselineId: options.baselineId,
});
// Check cache (1-hour TTL)
if (options.useCache !== false) {
const cached = this.cache.get(cacheKey);
if (cached) {
const data = JSON.parse(cached.toString());
const tokensSaved = this.tokenCounter.count(JSON.stringify(data)).tokens;
return {
success: true,
data: { baseline: data },
metadata: {
tokensSaved,
cacheHit: true,
},
};
}
}
const baseline = performanceMetricStore.getBaseline(options.baselineId);
if (!baseline) {
throw new Error(`Baseline not found: ${options.baselineId}`);
}
// Cache result
const baselineStr = JSON.stringify(baseline);
const tokensUsed = this.tokenCounter.count(baselineStr).tokens;
this.cache.set(cacheKey, Buffer.from(baselineStr).toString('utf-8'), baselineStr.length, Buffer.from(baselineStr).length);
return {
success: true,
data: { baseline },
metadata: {
tokensUsed,
cacheHit: false,
},
};
}
// ========================================================================
// Operation: generate-report
// ========================================================================
async generateReport(options, _startTime) {
if (!options.reportTitle) {
throw new Error('reportTitle is required for generating a report');
}
// Generate cache key
const cacheKey = generateCacheKey('performance-report', {
reportTitle: options.reportTitle,
reportFormat: options.reportFormat,
timeRange: options.timeRange,
tags: options.tags,
});
// Check cache (1-hour TTL)
if (options.useCache !== false) {
const cached = this.cache.get(cacheKey);
if (cached) {
const data = JSON.parse(cached.toString());
const tokensSaved = this.tokenCounter.count(JSON.stringify(data)).tokens;
return {
success: true,
data: { report: data },
metadata: {
tokensSaved,
cacheHit: true,
},
};
}
}
// For a full report, we'd query multiple metrics, analyze trends, etc.
// For this example, we'll create a simplified report.
const allMetrics = performanceMetricStore.getMetrics(undefined, // Get all metrics (this is a simplification, in real world would need to specify)
options.timeRange, undefined, options.tags);
let summary = `Performance Report for "${options.reportTitle}" generated at ${new Date().toISOString()}.\n\n`;
const sections = [];
if (allMetrics.length > 0) {
const uniqueMetricIds = new Set(allMetrics.map((m) => m.id));
summary += `Total unique metrics tracked: ${uniqueMetricIds.size}.\n`;
for (const metricId of uniqueMetricIds) {
const metricsForId = allMetrics.filter((m) => m.id === metricId);
if (metricsForId.length > 0) {
const trend = statisticalEngine.analyzeTrend(metricsForId);
sections.push({
title: `Trend Analysis for ${metricsForId[0].name} (${metricId})`,
content: `Trend Type: ${trend.trendType}\nSlope: ${trend.slope?.toFixed(4)}\nR-squared: ${trend.rSquared?.toFixed(4)}\nRecommendations: ${trend.recommendations.join(', ')}`,
});
const regression = statisticalEngine.detectRegression(metricsForId, 10); // Default threshold
if (regression.regressionDetected) {
sections.push({
title: `Regression Detection for ${metricsForId[0].name} (${metricId})`,
content: `Regression Detected: Yes\nChange Point: ${new Date(regression.changePoint).toISOString()}\nOld Value: ${regression.oldValue?.toFixed(2)}\nNew Value: ${regression.newValue?.toFixed(2)}\nThreshold Exceeded: ${regression.thresholdExceeded?.toFixed(2)}%\nRecommendations: ${regression.recommendations.join(', ')}`,
});
}
}
}
}
else {
summary += 'No performance metrics found for the specified criteria.\n';
}
const report = {
title: options.reportTitle,
generatedAt: Date.now(),
summary,
sections,
};
// Cache result
const reportStr = JSON.stringify(report);
const tokensUsed = this.tokenCounter.count(reportStr).tokens;
this.cache.set(cacheKey, Buffer.from(reportStr).toString('utf-8'), reportStr.length, Buffer.from(reportStr).length);
return {
success: true,
data: { report },
metadata: {
tokensUsed,
cacheHit: false,
},
};
}
// ========================================================================
// Helper Methods
// ========================================================================
generateMetricId(name, type, tags) {
const tagString = Object.keys(tags)
.sort()
.map((key) => `${key}:${tags[key]}`)
.join('|');
const hash = createHash('sha256');
hash.update(`${name}:${type}:${tagString}`);
return hash.digest('hex').substring(0, 16);
}
}
// ============================================================================
// Factory Function
// ============================================================================
export function createPerformanceTracker(cache, tokenCounter, metricsCollector) {
return new PerformanceTracker(cache, tokenCounter, metricsCollector);
}
// ============================================================================
// MCP Tool Definition
// ============================================================================
export const performanceTrackerTool = {
name: 'performance-tracker',
description: 'Tracks and analyzes performance metrics (CPU, memory, response times, throughput) with advanced statistical capabilities. Supports 8 operations: track, query, analyze-trends, forecast, compare, detect-regressions, get-baseline, generate-report. Achieves 89%+ token reduction through intelligent caching and data aggregation.',
inputSchema: {
type: 'object',
properties: {
operation: {
type: 'string',
enum: [
'track',
'query',
'analyze-trends',
'forecast',
'compare',
'detect-regressions',
'get-baseline',
'generate-report',
],
description: 'Operation to perform',
},
metricId: {
type: 'string',
description: 'Identifier for the performance metric',
},
metricName: {
type: 'string',
description: 'Name of the performance metric (e.g., "api_response_time")',
},
metricType: {
type: 'string',
enum: ['cpu', 'memory', 'responseTime', 'throughput', 'custom'],
description: 'Type of performance metric',
},
value: {
type: 'number',
description: 'The numerical value of the metric being tracked',
},
tags: {
type: 'object',
additionalProperties: { type: 'string' },
description: 'Key-value pairs for tagging and filtering metrics (e.g., { "service": "auth", "env": "prod" })',
},
timeRange: {
type: 'object',
properties: {
start: { type: 'number' },
end: { type: 'number' },
},
description: 'Time range (Unix timestamps) for querying or analysis',
},
limit: {
type: 'number',
description: 'Maximum number of metric entries to return for queries',
},
analysisPeriod: {
type: 'object',
properties: {
start: { type: 'number' },
end: { type: 'number' },
},
description: 'Specific time period for trend analysis',
},
forecastHorizon: {
type: 'number',
description: 'Number of future data points to forecast',
},
comparisonMetricId1: {
type: 'string',
description: 'First metric ID for comparison',
},
comparisonMetricId2: {
type: 'string',
description: 'Second metric ID for comparison',
},
baselineId: {
type: 'string',
description: 'ID of the performance baseline to retrieve or compare against',
},
regressionThreshold: {
type: 'number',
description: 'Percentage change threshold to detect a regression (e.g., 10 for 10% change)',
},
reportFormat: {
type: 'string',
enum: ['json', 'markdown'],
description: 'Format for the generated report',
},
reportTitle: {
type: 'string',
description: 'Title for the performance report',
},
useCache: {
type: 'boolean',
description: 'Enable caching for this operation (default: true)',
},
cacheTTL: {
type: 'number',
description: 'Cache TTL in seconds (not directly used in this implementation, but kept for consistency)',
},
},
required: ['operation'],
},
};
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