@emmahyde/thought-patterns
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MCP server combining systematic thinking, mental models, debugging approaches, and stochastic algorithms for comprehensive cognitive pattern support
100 lines (99 loc) • 4.52 kB
JavaScript
import { BaseToolServer } from '../base/BaseToolServer.js';
import { DecisionFrameworkSchema } from '../schemas/index.js';
import { boxed } from '../utils/index.js';
/**
* Decision Framework Server using clear-thought tools approach
* Extends BaseToolServer for standardized validation and error handling
*/
export class DecisionFrameworkServer extends BaseToolServer {
constructor() {
super(DecisionFrameworkSchema);
}
handle(validInput) {
return this.process(validInput);
}
/**
* Standardized process method for decision framework
* @param validInput - Validated decision framework data
* @returns Processed decision framework result
*/
process(validInput) {
// Format output using boxed utility
const formattedOutput = this.formatDecisionOutput(validInput);
// Log formatted output to console (suppress during tests)
if (process.env.NODE_ENV !== 'test' && process.env.JEST_WORKER_ID === undefined) {
console.error(formattedOutput);
}
return {
decisionStatement: validInput.decisionStatement,
decisionId: validInput.decisionId,
analysisType: validInput.analysisType,
stage: validInput.stage,
iteration: validInput.iteration,
nextStageNeeded: validInput.nextStageNeeded,
status: 'success',
optionCount: validInput.options.length,
criteriaCount: validInput.criteria?.length ?? 0,
hasRecommendation: !!validInput.recommendation,
timestamp: new Date().toISOString(),
framework: 'clear-thought-tools'
};
}
formatDecisionOutput(data) {
const sections = {
'Decision': data.decisionStatement,
'Analysis Type': data.analysisType.replace('-', ' ').toUpperCase(),
'Stage': data.stage.replace('-', ' ').toUpperCase(),
'Decision ID': data.decisionId,
'Iteration': data.iteration.toString()
};
// Options
if (data.options.length > 0) {
sections['Options'] = data.options.map(option => `• ${option.name}: ${option.description}`);
}
// Criteria
if (data.criteria && data.criteria.length > 0) {
sections['Criteria'] = data.criteria.map(criterion => `• ${criterion.name} (weight: ${(criterion.weight * 100).toFixed(0)}%): ${criterion.description}`);
}
// Stakeholders
if (data.stakeholders && data.stakeholders.length > 0) {
sections['Stakeholders'] = data.stakeholders.map(stakeholder => `• ${stakeholder}`);
}
// Constraints
if (data.constraints && data.constraints.length > 0) {
sections['Constraints'] = data.constraints.map(constraint => `• ${constraint}`);
}
// Time horizon and risk tolerance
if (data.timeHorizon) {
sections['Time Horizon'] = data.timeHorizon;
}
if (data.riskTolerance) {
sections['Risk Tolerance'] = data.riskTolerance.replace('-', ' ').toUpperCase();
}
// Expected values
if (data.expectedValues && Object.keys(data.expectedValues).length > 0) {
sections['Expected Values'] = Object.entries(data.expectedValues).map(([option, value]) => `• ${option}: ${value.toFixed(3)}`);
}
// Multi-criteria scores
if (data.multiCriteriaScores && Object.keys(data.multiCriteriaScores).length > 0) {
sections['Multi-Criteria Scores'] = Object.entries(data.multiCriteriaScores).map(([option, score]) => `• ${option}: ${score.toFixed(3)}`);
}
// Information gaps
if (data.informationGaps && data.informationGaps.length > 0) {
sections['Information Gaps'] = data.informationGaps.map(gap => `• ${gap.description} (impact: ${(gap.impact * 100).toFixed(0)}%)`);
}
// Sensitivity insights
if (data.sensitivityInsights && data.sensitivityInsights.length > 0) {
sections['Sensitivity Insights'] = data.sensitivityInsights.map(insight => `• ${insight}`);
}
// Recommendation
if (data.recommendation) {
sections['Recommendation'] = data.recommendation;
}
// Suggested next stage
if (data.suggestedNextStage) {
sections['Suggested Next Stage'] = data.suggestedNextStage;
}
return boxed('📊 Decision Framework', sections);
}
}