strategic-intelligence-mcp
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Strategic Intelligence MCP Server - connecting technical progress to business outcomes with systematic strategic planning
524 lines • 25.9 kB
JavaScript
import { TechnicalMilestoneTracker } from '../intelligence/technicalMilestoneTracker.js';
import { ProgressCorrelationEngine } from '../intelligence/progressCorrelationEngine.js';
import { AdvancedForecastingEngine } from '../forecasting/advancedForecastingEngine.js';
import { CompetitiveIntelligenceTracker } from '../forecasting/competitiveIntelligenceTracker.js';
export class ForecastingTools {
storage;
milestoneTracker;
correlationEngine;
forecastingEngine;
competitiveTracker;
constructor(storage) {
this.storage = storage;
this.milestoneTracker = new TechnicalMilestoneTracker(storage);
this.correlationEngine = new ProgressCorrelationEngine();
this.forecastingEngine = new AdvancedForecastingEngine();
this.competitiveTracker = new CompetitiveIntelligenceTracker();
}
async generateScenarioForecast(args = { timeframe: '12-months' }) {
try {
const { timeframe, focusArea = 'all', includeDisruption = true } = args;
// Load data
await this.milestoneTracker.loadMilestones();
const data = await this.storage.load();
const milestones = this.milestoneTracker.getAllMilestones();
const businessGoals = Object.values(data.goals || {});
// Generate correlations
const correlations = [];
for (const milestone of milestones) {
const milestoneCorrelations = this.correlationEngine.analyzeCorrelation(milestone, businessGoals);
correlations.push(...milestoneCorrelations);
}
// Generate multi-scenario forecast
const scenarios = this.forecastingEngine.generateMultiScenarioForecast(milestones, businessGoals, correlations, timeframe, focusArea);
// Calculate weighted forecast
const weightedForecast = this.calculateWeightedForecast(scenarios);
// Generate strategic recommendations
const recommendations = this.generateForecastRecommendations(scenarios, weightedForecast);
return {
success: true,
data: {
timeframe,
focusArea,
scenarios: includeDisruption ? scenarios : scenarios.filter(s => !s.id.includes('disruption')),
weightedForecast,
recommendations,
keyInsights: this.extractKeyInsights(scenarios),
confidenceAnalysis: this.analyzeConfidenceLevels(scenarios)
},
message: `Generated ${scenarios.length} scenario forecasts for ${timeframe} timeframe with balanced optimism/pessimism weighting (25%/50%/25%)`
};
}
catch (error) {
return {
success: false,
error: `Failed to generate scenario forecast: ${error}`
};
}
}
async identifyStrategyGaps(args = {}) {
try {
const { marketContext, minSeverity = 'moderate' } = args;
// Load data
await this.milestoneTracker.loadMilestones();
const data = await this.storage.load();
const milestones = this.milestoneTracker.getAllMilestones();
const businessGoals = Object.values(data.goals || {});
// Generate correlations
const correlations = [];
for (const milestone of milestones) {
const milestoneCorrelations = this.correlationEngine.analyzeCorrelation(milestone, businessGoals);
correlations.push(...milestoneCorrelations);
}
// Identify strategy gaps
const allGaps = this.forecastingEngine.identifyStrategyGaps(milestones, businessGoals, correlations, marketContext);
// Filter by severity
const severityLevels = ['minor', 'moderate', 'significant', 'critical'];
const minSeverityIndex = severityLevels.indexOf(minSeverity);
const filteredGaps = allGaps.filter(gap => severityLevels.indexOf(gap.severity) >= minSeverityIndex);
// Calculate total impact
const totalImpact = {
revenueAtRisk: filteredGaps.reduce((sum, gap) => sum + gap.estimatedImpact.revenueAtRisk, 0),
opportunityCost: filteredGaps.reduce((sum, gap) => sum + gap.estimatedImpact.opportunityCost, 0)
};
// Group gaps by category
const gapsByCategory = this.groupGapsByCategory(filteredGaps);
return {
success: true,
data: {
totalGaps: filteredGaps.length,
gapsByCategory,
totalImpact,
gaps: filteredGaps,
actionPlan: this.generateGapActionPlan(filteredGaps)
},
message: `Identified ${filteredGaps.length} strategy gaps with ${minSeverity}+ severity. Total revenue at risk: $${totalImpact.revenueAtRisk.toLocaleString()}`
};
}
catch (error) {
return {
success: false,
error: `Failed to identify strategy gaps: ${error}`
};
}
}
async generateCompetitiveIntelligence(args = {}) {
try {
// Load data
await this.milestoneTracker.loadMilestones();
const data = await this.storage.load();
const milestones = this.milestoneTracker.getAllMilestones();
const businessGoals = Object.values(data.goals || {});
// Generate competitive intelligence
const intelligence = this.competitiveTracker.generateCompetitiveIntelligence(milestones, businessGoals, {
segment: args.marketSegment,
size: args.marketSize,
competitors: args.competitors,
trends: args.trends
});
// Generate summary insights
const summaryInsights = {
overallThreatLevel: intelligence.threatAssessment.overallThreatLevel,
criticalThreats: intelligence.threatAssessment.immediateThreats.filter(t => t.potentialImpact.revenueAtRisk > 100000).length,
majorOpportunities: intelligence.opportunityMapping.filter(o => o.potentialReturn > 100000).length,
competitiveAdvantages: this.identifyCompetitiveAdvantages(intelligence),
urgentActions: intelligence.strategicRecommendations.filter(r => r.priority === 'critical')
};
return {
success: true,
data: {
intelligence,
summaryInsights,
competitivePositioning: this.assessCompetitivePositioning(intelligence),
timeHorizonAnalysis: this.analyzeTimeHorizons(intelligence)
},
message: `Generated competitive intelligence with ${intelligence.competitorProfiles.length} competitor profiles and ${intelligence.strategicRecommendations.length} strategic recommendations`
};
}
catch (error) {
return {
success: false,
error: `Failed to generate competitive intelligence: ${error}`
};
}
}
async runWhatIfAnalysis(args) {
try {
const { scenarios, timeframe } = args;
// Load base data
await this.milestoneTracker.loadMilestones();
const data = await this.storage.load();
const milestones = this.milestoneTracker.getAllMilestones();
const businessGoals = Object.values(data.goals || {});
// Generate base correlations
const correlations = [];
for (const milestone of milestones) {
const milestoneCorrelations = this.correlationEngine.analyzeCorrelation(milestone, businessGoals);
correlations.push(...milestoneCorrelations);
}
// Run what-if analysis for each scenario
const whatIfResults = [];
for (const scenario of scenarios) {
// Apply scenario adjustments
const adjustedMilestones = this.applyScenarioAdjustments(milestones, scenario.assumptions);
const adjustedCorrelations = this.adjustCorrelations(correlations, scenario.assumptions);
// Generate forecast with adjustments
const scenarioForecasts = this.forecastingEngine.generateMultiScenarioForecast(adjustedMilestones, businessGoals, adjustedCorrelations, timeframe);
// Calculate impact
const baseForecasts = this.forecastingEngine.generateMultiScenarioForecast(milestones, businessGoals, correlations, timeframe);
const impact = this.calculateScenarioImpact(baseForecasts, scenarioForecasts);
whatIfResults.push({
scenario: scenario.name,
description: scenario.description,
assumptions: scenario.assumptions,
impact,
keyMetrics: this.extractScenarioKeyMetrics(scenarioForecasts),
recommendations: this.generateScenarioRecommendations(scenario, impact)
});
}
// Generate comparative analysis
const comparativeAnalysis = this.compareWhatIfScenarios(whatIfResults);
return {
success: true,
data: {
scenarios: whatIfResults,
comparativeAnalysis,
bestCaseScenario: whatIfResults.reduce((best, current) => current.impact.revenueImpact > best.impact.revenueImpact ? current : best),
worstCaseScenario: whatIfResults.reduce((worst, current) => current.impact.revenueImpact < worst.impact.revenueImpact ? current : worst)
},
message: `Completed what-if analysis for ${scenarios.length} scenarios over ${timeframe} timeframe`
};
}
catch (error) {
return {
success: false,
error: `Failed to run what-if analysis: ${error}`
};
}
}
async generateConfidenceIntervals(args) {
try {
const { metric, timeframes, confidenceLevels = [50, 75, 90] } = args;
// Load data
await this.milestoneTracker.loadMilestones();
const data = await this.storage.load();
const milestones = this.milestoneTracker.getAllMilestones();
const businessGoals = Object.values(data.goals || {});
// Generate correlations
const correlations = [];
for (const milestone of milestones) {
const milestoneCorrelations = this.correlationEngine.analyzeCorrelation(milestone, businessGoals);
correlations.push(...milestoneCorrelations);
}
const intervals = [];
for (const timeframe of timeframes) {
// Generate scenarios for this timeframe
const scenarios = this.forecastingEngine.generateMultiScenarioForecast(milestones, businessGoals, correlations, timeframe);
// Calculate confidence intervals
const metricValues = this.extractMetricValues(scenarios, metric);
const intervalData = {
timeframe,
metric,
intervals: confidenceLevels.map(level => ({
confidenceLevel: level,
lowerBound: this.calculatePercentile(metricValues, (100 - level) / 2),
upperBound: this.calculatePercentile(metricValues, 100 - (100 - level) / 2),
median: this.calculatePercentile(metricValues, 50),
spread: 0 // Will calculate below
}))
};
// Calculate spread
intervalData.intervals.forEach(interval => {
interval.spread = interval.upperBound - interval.lowerBound;
});
intervals.push(intervalData);
}
// Generate insights about confidence
const confidenceInsights = this.generateConfidenceInsights(intervals, metric);
return {
success: true,
data: {
metric,
intervals,
confidenceInsights,
recommendations: this.generateConfidenceRecommendations(intervals, metric)
},
message: `Generated confidence intervals for ${metric} across ${timeframes.length} timeframes with balanced optimism/pessimism`
};
}
catch (error) {
return {
success: false,
error: `Failed to generate confidence intervals: ${error}`
};
}
}
// Helper methods
calculateWeightedForecast(scenarios) {
const weights = {
'Base Case (Realistic)': 0.5,
'Conservative (Defensive Planning)': 0.25,
'Optimistic (Upside Case)': 0.25
};
const weighted = {
projectedRevenue: 0,
customerAcquisition: 0,
marketShare: 0,
milestonesCompleted: 0
};
scenarios.forEach(scenario => {
const weight = weights[scenario.name] || 0;
if (weight > 0) {
weighted.projectedRevenue += scenario.businessMetrics.projectedRevenue.realistic * weight;
weighted.customerAcquisition += scenario.businessMetrics.customerAcquisition.realistic * weight;
weighted.marketShare += scenario.businessMetrics.marketShare.realistic * weight;
weighted.milestonesCompleted += scenario.technicalMetrics.milestonesCompleted.realistic * weight;
}
});
return {
projectedRevenue: Math.round(weighted.projectedRevenue),
customerAcquisition: Math.round(weighted.customerAcquisition),
marketShare: weighted.marketShare.toFixed(2),
milestonesCompleted: Math.round(weighted.milestonesCompleted)
};
}
generateForecastRecommendations(scenarios, weighted) {
const recommendations = [];
// Revenue recommendations
if (weighted.projectedRevenue < 100000) {
recommendations.push('Focus on revenue-generating features to improve projections');
}
// Customer acquisition recommendations
if (weighted.customerAcquisition < 10) {
recommendations.push('Accelerate go-to-market strategy to increase customer acquisition');
}
// Risk mitigation recommendations
const highRisks = scenarios.flatMap(s => s.riskFactors.filter((r) => r.impact === 'critical'));
if (highRisks.length > 0) {
recommendations.push('Implement mitigation strategies for critical risks');
}
return recommendations;
}
extractKeyInsights(scenarios) {
const insights = [];
// Compare optimistic vs pessimistic spreads
const optimistic = scenarios.find(s => s.name.includes('Optimistic'));
const pessimistic = scenarios.find(s => s.name.includes('Conservative'));
if (optimistic && pessimistic) {
const revenueSpread = optimistic.businessMetrics.projectedRevenue.optimistic -
pessimistic.businessMetrics.projectedRevenue.conservative;
insights.push({
type: 'variance',
insight: `Revenue projections vary by $${revenueSpread.toLocaleString()} between best and worst cases`,
implication: 'High variance suggests need for adaptive planning'
});
}
return insights;
}
analyzeConfidenceLevels(scenarios) {
const confidences = scenarios.map(s => ({
scenario: s.name,
confidence: s.confidence,
uncertaintyRange: s.uncertaintyRange
}));
return {
averageConfidence: confidences.reduce((sum, c) => sum + c.confidence, 0) / confidences.length,
maxUncertainty: Math.max(...confidences.map(c => c.uncertaintyRange)),
confidenceDistribution: confidences
};
}
groupGapsByCategory(gaps) {
const grouped = {};
gaps.forEach(gap => {
grouped[gap.category] = (grouped[gap.category] || 0) + 1;
});
return grouped;
}
generateGapActionPlan(gaps) {
const criticalGaps = gaps.filter(g => g.severity === 'critical' || g.urgency === 'critical');
const highPriorityGaps = gaps.filter(g => g.severity === 'significant' || g.urgency === 'high');
return {
immediate: criticalGaps.map(g => g.recommendedActions[0]),
shortTerm: highPriorityGaps.map(g => g.recommendedActions[0]),
totalCost: gaps.reduce((sum, g) => sum + (g.recommendedActions[0]?.cost || 0), 0),
totalEffort: this.aggregateEffort(gaps)
};
}
aggregateEffort(gaps) {
const effortLevels = gaps.map(g => g.recommendedActions[0]?.effort || 'medium');
const highEffortCount = effortLevels.filter(e => e === 'high').length;
if (highEffortCount > gaps.length / 2)
return 'high';
if (highEffortCount > 0)
return 'medium-high';
return 'medium';
}
identifyCompetitiveAdvantages(intelligence) {
const advantages = [];
// Privacy advantage
if (intelligence.marketAnalysis.keyTrends.some((t) => t.id === 'privacy-regulations' && t.ourPositioning === 'advantage')) {
advantages.push('Privacy-by-design architecture');
}
// AI philosophy advantage
const aiWeaknesses = intelligence.competitorProfiles
.flatMap((c) => c.weaknessAreas)
.filter((w) => w.area === 'AI Philosophy');
if (aiWeaknesses.length > 0) {
advantages.push('Bounded enhancement philosophy');
}
return advantages;
}
assessCompetitivePositioning(intelligence) {
const threats = intelligence.threatAssessment.immediateThreats;
const opportunities = intelligence.opportunityMapping;
return {
position: this.determineMarketPosition(threats, opportunities),
vulnerabilities: intelligence.threatAssessment.strategicVulnerabilities.length,
advantages: this.identifyCompetitiveAdvantages(intelligence).length,
netPosition: opportunities.length - threats.length
};
}
determineMarketPosition(threats, opportunities) {
const threatScore = threats.reduce((sum, t) => sum + t.probability / 100, 0);
const opportunityScore = opportunities.reduce((sum, o) => sum + o.potentialReturn / 100000, 0);
if (opportunityScore > threatScore * 1.5)
return 'strong';
if (opportunityScore > threatScore)
return 'favorable';
if (threatScore > opportunityScore * 1.5)
return 'vulnerable';
return 'balanced';
}
analyzeTimeHorizons(intelligence) {
const immediateActions = intelligence.strategicRecommendations
.filter((r) => r.timeframe.includes('2-4') || r.timeframe.includes('3-6'));
const mediumTermActions = intelligence.strategicRecommendations
.filter((r) => r.timeframe.includes('6-12'));
return {
immediate: immediateActions.length,
mediumTerm: mediumTermActions.length,
criticalTimingRisks: intelligence.threatAssessment.immediateThreats
.filter((t) => t.timeToMaterialize.includes('3-6') || t.timeToMaterialize.includes('1-3'))
.length
};
}
applyScenarioAdjustments(milestones, assumptions) {
// Clone milestones and apply adjustments
return milestones.map(m => ({
...m,
// Adjust completion likelihood based on assumptions
status: this.adjustMilestoneStatus(m.status, assumptions.completionRateChange)
}));
}
adjustMilestoneStatus(currentStatus, completionRateChange) {
if (!completionRateChange || currentStatus === 'completed')
return currentStatus;
// Simple adjustment logic
if (completionRateChange > 20 && currentStatus === 'in-progress') {
return 'completed';
}
if (completionRateChange < -20 && currentStatus === 'in-progress') {
return 'delayed';
}
return currentStatus;
}
adjustCorrelations(correlations, assumptions) {
const revenueMultiplier = 1 + (assumptions.revenueRealizationChange || 0) / 100;
return correlations.map(c => ({
...c,
correlationStrength: Math.min(100, Math.max(0, c.correlationStrength * revenueMultiplier))
}));
}
calculateScenarioImpact(baseForecasts, scenarioForecasts) {
const baseRevenue = this.calculateWeightedForecast(baseForecasts).projectedRevenue;
const scenarioRevenue = this.calculateWeightedForecast(scenarioForecasts).projectedRevenue;
return {
revenueImpact: scenarioRevenue - baseRevenue,
percentageChange: ((scenarioRevenue - baseRevenue) / baseRevenue) * 100
};
}
extractScenarioKeyMetrics(forecasts) {
const weighted = this.calculateWeightedForecast(forecasts);
return {
revenue: weighted.projectedRevenue,
customers: weighted.customerAcquisition,
marketShare: weighted.marketShare,
milestones: weighted.milestonesCompleted
};
}
generateScenarioRecommendations(scenario, impact) {
const recommendations = [];
if (impact.revenueImpact > 50000) {
recommendations.push(`Pursue strategies aligned with "${scenario.name}" scenario`);
}
if (impact.percentageChange < -20) {
recommendations.push(`Develop contingency plans for "${scenario.name}" scenario`);
}
return recommendations;
}
compareWhatIfScenarios(results) {
return {
bestPerformer: results.reduce((best, current) => current.impact.revenueImpact > best.impact.revenueImpact ? current.scenario : best.scenario, results[0]?.scenario),
riskiestScenario: results.reduce((riskiest, current) => current.impact.revenueImpact < riskiest.impact.revenueImpact ? current.scenario : riskiest.scenario, results[0]?.scenario),
revenueRange: {
min: Math.min(...results.map(r => r.keyMetrics.revenue)),
max: Math.max(...results.map(r => r.keyMetrics.revenue))
}
};
}
extractMetricValues(scenarios, metric) {
const values = [];
scenarios.forEach(scenario => {
switch (metric) {
case 'revenue':
values.push(scenario.businessMetrics.projectedRevenue.conservative, scenario.businessMetrics.projectedRevenue.realistic, scenario.businessMetrics.projectedRevenue.optimistic);
break;
case 'customer-acquisition':
values.push(scenario.businessMetrics.customerAcquisition.conservative, scenario.businessMetrics.customerAcquisition.realistic, scenario.businessMetrics.customerAcquisition.optimistic);
break;
case 'market-share':
values.push(scenario.businessMetrics.marketShare.conservative, scenario.businessMetrics.marketShare.realistic, scenario.businessMetrics.marketShare.optimistic);
break;
case 'milestone-completion':
values.push(scenario.technicalMetrics.milestonesCompleted.conservative, scenario.technicalMetrics.milestonesCompleted.realistic, scenario.technicalMetrics.milestonesCompleted.optimistic);
break;
}
});
return values.sort((a, b) => a - b);
}
calculatePercentile(values, percentile) {
const index = (percentile / 100) * (values.length - 1);
const lower = Math.floor(index);
const upper = Math.ceil(index);
const weight = index % 1;
if (lower === upper) {
return values[lower];
}
return values[lower] * (1 - weight) + values[upper] * weight;
}
generateConfidenceInsights(intervals, metric) {
const insights = [];
// Analyze spread growth over time
const spreadGrowth = intervals.map(i => i.intervals[0].spread);
if (spreadGrowth.length > 1) {
const growthRate = (spreadGrowth[spreadGrowth.length - 1] - spreadGrowth[0]) / spreadGrowth[0];
insights.push({
type: 'uncertainty-growth',
insight: `Uncertainty for ${metric} increases by ${(growthRate * 100).toFixed(1)}% over time`,
implication: growthRate > 0.5 ? 'High uncertainty growth requires adaptive planning' : 'Moderate uncertainty is manageable'
});
}
return insights;
}
generateConfidenceRecommendations(intervals, metric) {
const recommendations = [];
// Check if uncertainty is too high
const maxSpread = Math.max(...intervals.flatMap(i => i.intervals.map((int) => int.spread)));
const avgMedian = intervals.reduce((sum, i) => sum + i.intervals[0].median, 0) / intervals.length;
if (maxSpread > avgMedian * 0.5) {
recommendations.push(`High uncertainty in ${metric} projections - consider scenario planning`);
}
return recommendations;
}
}
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