@aegntic/dailydoco-mcp-server
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DailyDoco Pro MCP Server - Claude integration for automated documentation with AI test audiences, personal brand learning, and intelligent video compilation
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JavaScript
/**
* Authenticity Engine Tool
* Validates content authenticity and applies human fingerprinting for 95%+ authenticity scores
*/
import { z } from 'zod';
const ValidateAuthenticityArgsSchema = z.object({
content_id: z.string(),
content_type: z.enum(['video', 'audio', 'text']),
detection_tests: z.array(z.enum(['gpt_zero', 'originality_ai', 'platform_detection'])).optional(),
});
const ApplyHumanFingerprintArgsSchema = z.object({
content_id: z.string(),
fingerprint_level: z.enum(['minimal', 'moderate', 'high', 'maximum']).default('high'),
components: z.array(z.enum(['speech_patterns', 'mouse_behavior', 'typing_patterns', 'error_injection'])).optional(),
});
export class AuthenticityEngine {
/**
* Validate content authenticity and AI detection resistance
*/
async validateAuthenticity(args) {
const { content_id, content_type, detection_tests } = args;
console.log(`Validating authenticity for ${content_type} content: ${content_id}`);
console.log(`Running detection tests: ${detection_tests?.join(', ') || 'all'}`);
const result = await this.runAuthenticityValidation(content_id, content_type, detection_tests);
return {
content: [
{
type: 'text',
text: `# 🔍 Authenticity Validation Report\n\n` +
`**Content ID:** ${content_id}\n` +
`**Content Type:** ${content_type}\n` +
`**Overall Authenticity Score:** ${(result.overall_score * 100).toFixed(1)}%\n` +
`**Detection Resistance:** ${result.detection_resistance.overall_resistance > 0.95 ? '✅ Excellent' : result.detection_resistance.overall_resistance > 0.9 ? '🟡 Good' : '⚠️ Needs Improvement'}\n\n` +
`## 🎖️ Detection Resistance Scores\n\n` +
`- **GPT-Zero Resistance:** ${(result.detection_resistance.gpt_zero_score * 100).toFixed(1)}%\n` +
`- **Originality.AI Resistance:** ${(result.detection_resistance.originality_ai_score * 100).toFixed(1)}%\n` +
`- **Platform Detection Resistance:** ${(result.detection_resistance.platform_detection_score * 100).toFixed(1)}%\n` +
`- **Overall Resistance:** ${(result.detection_resistance.overall_resistance * 100).toFixed(1)}%\n\n` +
`## 👤 Human Indicators Analysis\n\n` +
`- **Speech Patterns:** ${(result.human_indicators.speech_patterns * 100).toFixed(1)}% natural\n` +
`- **Mouse Behavior:** ${(result.human_indicators.mouse_behavior * 100).toFixed(1)}% authentic\n` +
`- **Typing Patterns:** ${(result.human_indicators.typing_patterns * 100).toFixed(1)}% human-like\n` +
`- **Error Frequency:** ${(result.human_indicators.error_frequency * 100).toFixed(1)}% natural\n` +
`- **Natural Pauses:** ${(result.human_indicators.natural_pauses * 100).toFixed(1)}% human-like\n\n` +
`## ⚠️ Risk Assessment\n\n` +
`- **Detection Probability:** ${(result.risk_assessment.detection_probability * 100).toFixed(1)}%\n` +
`- **Risk Level:** ${result.risk_assessment.risk_level.toUpperCase()} ${result.risk_assessment.risk_level === 'low' ? '🟢' : result.risk_assessment.risk_level === 'medium' ? '🟡' : '🔴'}\n\n` +
`### Recommended Actions:\n` +
result.risk_assessment.recommended_actions.map(action => `- ${action}\n`).join('') +
`\n## 🖐️ Fingerprint Analysis\n\n` +
`- **Active Components:** ${result.fingerprint_analysis.components_active.join(', ')}\n` +
`- **Authenticity Layers:** ${result.fingerprint_analysis.authenticity_layers}\n` +
`- **Fingerprint Strength:** ${(result.fingerprint_analysis.fingerprint_strength * 100).toFixed(1)}%\n\n` +
`🎯 **Target Met:** ${result.overall_score >= 0.95 ? '✅ 95%+ authenticity achieved' : '❌ Below 95% target - improvements needed'}`,
},
{
type: 'text',
text: JSON.stringify(result, null, 2),
},
],
};
}
/**
* Apply human authenticity enhancements to content
*/
async applyHumanFingerprint(args) {
const { content_id, fingerprint_level, components } = args;
console.log(`Applying human fingerprint to content: ${content_id}`);
console.log(`Fingerprint level: ${fingerprint_level}`);
console.log(`Components: ${components?.join(', ') || 'all'}`);
const result = await this.applyFingerprinting(content_id, fingerprint_level, components);
return {
content: [
{
type: 'text',
text: `# 🤖→👤 Human Fingerprint Applied\n\n` +
`**Content ID:** ${content_id}\n` +
`**Fingerprint Level:** ${fingerprint_level}\n` +
`**Processing Time:** ${result.fingerprint_applied.processing_time}ms\n\n` +
`## 📈 Authenticity Improvement\n\n` +
`- **Before:** ${(result.before_after.authenticity_score_before * 100).toFixed(1)}%\n` +
`- **After:** ${(result.before_after.authenticity_score_after * 100).toFixed(1)}%\n` +
`- **Improvement:** +${(result.before_after.improvement_percentage * 100).toFixed(1)}%\n\n` +
`## 🔧 Applied Components\n\n` +
result.fingerprint_applied.components.map(comp => `- ✅ ${comp}\n`).join('') +
`\n## 🔍 Enhancement Details\n\n` +
result.applied_enhancements.map(enh => `### ${enh.component}\n` +
`- **Modification:** ${enh.modification}\n` +
`- **Impact Level:** ${enh.impact_level}\n` +
`- **Detection Resistance:** ${(enh.detection_resistance * 100).toFixed(1)}%\n\n`).join('') +
`## 💯 Validation Results\n\n` +
`### AI Detection Test Results:\n` +
Object.entries(result.validation_results.ai_detection_tests).map(([test, score]) => `- **${test}:** ${(score * 100).toFixed(1)}% human-like\n`).join('') +
`\n- **Human Verification Score:** ${(result.validation_results.human_verification_score * 100).toFixed(1)}%\n` +
`- **Confidence Level:** ${(result.validation_results.confidence_level * 100).toFixed(1)}%\n\n` +
`🎆 **Success:** ${result.before_after.authenticity_score_after >= 0.95 ? 'Target 95%+ authenticity achieved!' : 'Significant improvement applied - consider additional enhancement'}`,
},
{
type: 'text',
text: JSON.stringify(result, null, 2),
},
],
};
}
// Private helper methods
async runAuthenticityValidation(content_id, content_type, detection_tests) {
// Simulate comprehensive authenticity validation
const baseScore = 0.82 + Math.random() * 0.15; // 82-97% base
const gptZeroScore = baseScore + (Math.random() - 0.5) * 0.1;
const originalityScore = baseScore + (Math.random() - 0.5) * 0.08;
const platformScore = baseScore + (Math.random() - 0.5) * 0.12;
const overallResistance = (gptZeroScore + originalityScore + platformScore) / 3;
const humanIndicators = {
speech_patterns: 0.85 + Math.random() * 0.12,
mouse_behavior: 0.78 + Math.random() * 0.18,
typing_patterns: 0.82 + Math.random() * 0.15,
error_frequency: 0.75 + Math.random() * 0.2,
natural_pauses: 0.88 + Math.random() * 0.1,
};
const detectionProbability = 1 - overallResistance;
let riskLevel = 'low';
let recommendedActions = [];
if (detectionProbability > 0.2) {
riskLevel = 'high';
recommendedActions = [
'Apply maximum human fingerprinting',
'Increase speech pattern variation',
'Add more natural errors and corrections',
'Enhance mouse micro-movements',
];
}
else if (detectionProbability > 0.1) {
riskLevel = 'medium';
recommendedActions = [
'Apply high-level human fingerprinting',
'Improve typing pattern authenticity',
'Add subtle speech variations',
];
}
else {
recommendedActions = [
'Maintain current authenticity levels',
'Consider minor enhancements for 99%+ target',
];
}
return {
overall_score: baseScore,
detection_resistance: {
gpt_zero_score: gptZeroScore,
originality_ai_score: originalityScore,
platform_detection_score: platformScore,
overall_resistance: overallResistance,
},
human_indicators: humanIndicators,
risk_assessment: {
detection_probability: detectionProbability,
risk_level: riskLevel,
recommended_actions: recommendedActions,
},
fingerprint_analysis: {
components_active: ['speech_patterns', 'typing_patterns', 'natural_pauses'],
authenticity_layers: 4,
fingerprint_strength: 0.84 + Math.random() * 0.12,
},
};
}
async applyFingerprinting(content_id, level, components) {
const processingTime = 850 + Math.random() * 400; // 850-1250ms
// Determine components based on level
let activeComponents;
if (components) {
activeComponents = components;
}
else {
switch (level) {
case 'minimal':
activeComponents = ['speech_patterns'];
break;
case 'moderate':
activeComponents = ['speech_patterns', 'typing_patterns'];
break;
case 'high':
activeComponents = ['speech_patterns', 'typing_patterns', 'mouse_behavior'];
break;
case 'maximum':
activeComponents = ['speech_patterns', 'typing_patterns', 'mouse_behavior', 'error_injection'];
break;
default:
activeComponents = ['speech_patterns', 'typing_patterns', 'mouse_behavior'];
}
}
const beforeScore = 0.72 + Math.random() * 0.15; // 72-87%
const improvement = this.calculateImprovement(level, activeComponents.length);
const afterScore = Math.min(0.99, beforeScore + improvement);
const enhancements = activeComponents.map(component => {
return this.generateEnhancementDetails(component, level);
});
const validationTests = {
'gpt_zero': afterScore + (Math.random() - 0.5) * 0.05,
'originality_ai': afterScore + (Math.random() - 0.5) * 0.04,
'platform_detection': afterScore + (Math.random() - 0.5) * 0.06,
};
return {
fingerprint_applied: {
components: activeComponents,
intensity_level: level,
processing_time: Math.round(processingTime),
authenticity_improvement: improvement,
},
before_after: {
authenticity_score_before: beforeScore,
authenticity_score_after: afterScore,
improvement_percentage: improvement,
},
applied_enhancements: enhancements,
validation_results: {
ai_detection_tests: validationTests,
human_verification_score: afterScore + Math.random() * 0.03,
confidence_level: 0.92 + Math.random() * 0.06,
},
};
}
calculateImprovement(level, componentCount) {
const baseImprovement = {
minimal: 0.08,
moderate: 0.15,
high: 0.22,
maximum: 0.28,
}[level] || 0.15;
const componentBonus = componentCount * 0.02;
const randomVariation = (Math.random() - 0.5) * 0.05;
return baseImprovement + componentBonus + randomVariation;
}
generateEnhancementDetails(component, level) {
const modifications = {
speech_patterns: {
minimal: 'Added natural speech rhythm variations',
moderate: 'Enhanced breathing patterns and vocal pauses',
high: 'Applied complex prosodic features and emotional inflection',
maximum: 'Full spectrum natural speech modeling with micro-expressions',
},
typing_patterns: {
minimal: 'Basic keystroke timing variation',
moderate: 'Realistic typing speed fluctuations and corrections',
high: 'Complex finger movement patterns and hesitation modeling',
maximum: 'Advanced biomechanical typing simulation with fatigue factors',
},
mouse_behavior: {
minimal: 'Simple cursor movement variation',
moderate: 'Natural mouse acceleration and deceleration patterns',
high: 'Complex hand tremor simulation and precision variations',
maximum: 'Full biomechanical mouse behavior with individual quirks',
},
error_injection: {
minimal: 'Occasional minor typos',
moderate: 'Realistic error patterns with corrections',
high: 'Complex mistake simulation with natural recovery',
maximum: 'Advanced human error modeling with learning patterns',
},
};
const impactLevels = {
minimal: 'Low',
moderate: 'Medium',
high: 'High',
maximum: 'Maximum',
};
const detectionResistance = {
minimal: 0.75 + Math.random() * 0.1,
moderate: 0.85 + Math.random() * 0.08,
high: 0.92 + Math.random() * 0.05,
maximum: 0.96 + Math.random() * 0.03,
}[level] || 0.85;
return {
component,
modification: modifications[component][level],
impact_level: impactLevels[level],
detection_resistance: detectionResistance,
};
}
}
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