Prologue: How We Built Self-Discovering AI Agent Onboarding

// Prologue Discovery Engine Architecture
interface DiscoveryEngine {
  scanner: MCPServerScanner;
  evaluator: ServerEvaluator;
  indexer: ServerIndexer;
  recommender: IntelligentRecommender;
}

class MCPServerScanner {
  private sources: DiscoverySource[] = [
    new GitHubScanner(),
    new MCPRegistryScanner(),
    new CommunityScanner(),
    new DocumentationScanner()
  ];

  async discoverServers(): Promise<MCPServer[]> {
    const discoveries = await Promise.all(
      this.sources.map(source => source.scan())
    );

    return this.deduplicateAndMerge(discoveries.flat());
  }
}

class ServerEvaluator {
  private criteria: EvaluationCriteria = {
    codeQuality: 0.25,
    maintenance: 0.20,
    community: 0.15,
    documentation: 0.15,
    performance: 0.15,
    compatibility: 0.10
  };

  async evaluateServer(server: MCPServer): Promise<ServerScore> {
    const scores = await Promise.all([
      this.analyzeCodeQuality(server.repository),
      this.checkMaintenanceStatus(server),
      this.assessCommunitySupport(server),
      this.evaluateDocumentation(server),
      this.benchmarkPerformance(server),
      this.testCompatibility(server)
    ]);

    return this.calculateWeightedScore(scores, this.criteria);
  }
}

# Quality Scoring Formula (Patent-Pending)
def calculate_server_score(server):
    agentic_potential = assess_agentic_capabilities(server) * 0.4
    community_validation = (server.stars / 1000) * 0.3
    code_quality = analyze_code_metrics(server) * 0.2
    category_relevance = calculate_category_fit(server, use_case) * 0.1

    return {
        'overall_score': agentic_potential + community_validation +
                        code_quality + category_relevance,
        'breakdown': {
            'agentic_potential': agentic_potential,
            'community_validation': community_validation,
            'code_quality': code_quality,
            'category_relevance': category_relevance
        }
    }

// Platform Abstraction Layer
interface PlatformAdapter {
  name: string;
  commandSyntax: CommandSyntax;
  capabilities: PlatformCapabilities;
  integration: IntegrationMethod;
}

class ClaudeAdapter implements PlatformAdapter {
  name = "Claude (Code/Desktop)";
  commandSyntax = new ClaudeCommandSyntax();
  capabilities = new ClaudeCapabilities();

  async integrate(server: MCPServer): Promise<IntegrationResult> {
    // Claude-specific integration logic
    const config = this.generateClaudeConfig(server);
    const installation = await this.installClaudeExtension(server);
    const validation = await this.validateClaudeIntegration(server);

    return { config, installation, validation };
  }
}

class AuggieAdapter implements PlatformAdapter {
  name = "Auggie AI";
  commandSyntax = new AuggieCommandSyntax(); // !prologue prefix

  async integrate(server: MCPServer): Promise<IntegrationResult> {
    // Auggie-specific integration logic
    const command = this.generateAuggieCommand(server);
    const registration = await this.registerAuggieCommand(command);

    return { command, registration };
  }
}

class PlatformRegistry {
  private adapters: Map<string, PlatformAdapter> = new Map();

  constructor() {
    this.registerAdapter(new ClaudeAdapter());
    this.registerAdapter(new AuggieAdapter());
    this.registerAdapter(new TunaCodeAdapter());
    this.registerAdapter(new GeminiAdapter());
    this.registerAdapter(new OpenCodeAdapter());
    this.registerAdapter(new UniversalAdapter()); // Fallback for unknown platforms
  }

  async integrateAcrossPlatforms(server: MCPServer): Promise<PlatformIntegration[]> {
    const integrations = await Promise.allSettled(
      Array.from(this.adapters.values()).map(adapter =>
        adapter.integrate(server)
      )
    );

    return integrations
      .filter(result => result.status === 'fulfilled')
      .map(result => result.value);
  }
}

// Workflow Orchestration Engine
class WorkflowOptimizer {
  private graph: WorkflowGraph;
  private analyzer: WorkflowAnalyzer;

  async optimizeWorkflow(useCase: UseCase): Promise<OptimizedWorkflow> {
    // Analyze the use case requirements
    const requirements = await this.analyzer.analyze(useCase);

    // Generate potential server combinations
    const combinations = await this.generateCombinations(requirements);

    // Evaluate each combination
    const evaluations = await Promise.all(
      combinations.map(combo => this.evaluateCombination(combo, requirements))
    );

    // Select optimal workflow
    const best = evaluations.sort((a, b) => b.score - a.score)[0];

    return this.buildWorkflow(best.combination, best.optimizations);
  }

  private async generateCombinations(requirements: Requirements): Promise<ServerCombination[]> {
    // This is where the magic happens - intelligent server combination
    const servers = await this.discoveryEngine.findMatchingServers(requirements);

    return this.combinationGenerator.generate({
      servers: servers,
      requirements: requirements,
      constraints: {
        max_servers: 7, // Practical limit for complexity
        min_redundancy: 0, // Avoid unnecessary overlap
        required_categories: requirements.categories
      }
    });
  }
}

// Self-Discovering AI Agent
class SelfDiscoveringAgent {
  private prologue: PrologueEngine;
  private context: AgentContext;
  private capabilities: CapabilityRegistry;

  async initialize(context: AgentContext): Promise<void> {
    this.context = context;

    // Analyze agent's purpose and requirements
    const analysis = await this.analyzeRequirements(context);

    // Discover optimal toolchain
    const discovery = await this.prologue.discoverForAgent(analysis);

    // Integrate discovered capabilities
    await this.integrateCapabilities(discovery.tools);

    // Optimize workflow
    await this.optimizeWorkflow(discovery.workflow);
  }

  private async analyzeRequirements(context: AgentContext): Promise<AgentRequirements> {
    return {
      purpose: context.primaryObjective,
      domains: this.extractDomains(context),
      capabilities: this.inferRequiredCapabilities(context),
      constraints: this.identifyConstraints(context),
      preferences: this.extractPreferences(context)
    };
  }

  async adaptToNewChallenge(challenge: Challenge): Promise<AdaptationResult> {
    // Analyze the new challenge
    const gap = await this.identifyCapabilityGap(challenge);

    if (gap.requiresNewTools) {
      // Discover and integrate new tools
      const discovery = await this.prologue.discoverForChallenge(gap);
      await this.integrateCapabilities(discovery.tools);
    }

    if (gap.requiresOptimization) {
      // Optimize existing workflow
      await this.optimizeWorkflowForChallenge(challenge);
    }

    return { adapted: true, newCapabilities: gap.newToolsAdded };
  }
}

class PlatformDetector {
  detect(): PlatformInfo {
    // Check environment variables
    if (process.env.CLAUDE_API_KEY) {
      return { platform: 'claude', type: 'api' };
    }

    // Check process names
    if (process.argv.includes('claude-code')) {
      return { platform: 'claude', type: 'desktop' };
    }

    // Check terminal capabilities
    if (this.hasAuggieCapabilities()) {
      return { platform: 'auggie', type: 'terminal' };
    }

    // Check file system patterns
    if (this.hasTunaCodePatterns()) {
      return { platform: 'tunacode', type: 'ide' };
    }

    // Fallback to universal mode
    return { platform: 'universal', type: 'generic' };
  }
}

class IntelligentRecommender {
  async recommendServers(requirements: ServerRequirements): Promise<Recommendation[]> {
    // Find matching servers
    const candidates = await this.findMatchingServers(requirements);

    // Analyze interactions and dependencies
    const interactions = await this.analyzeInteractions(candidates);

    // Generate workflow combinations
    const workflows = await this.generateWorkflows(candidates, interactions);

    // Score each workflow
    const scored = await Promise.all(
      workflows.map(workflow => this.scoreWorkflow(workflow, requirements))
    );

    // Return top recommendations with explanations
    return scored
      .sort((a, b) => b.score - a.score)
      .slice(0, 5)
      .map(score => ({
        workflow: score.workflow,
        score: score.score,
        reasoning: score.reasoning,
        alternatives: score.alternatives
      }));
  }

  private async scoreWorkflow(workflow: Workflow, requirements: ServerRequirements): Promise<WorkflowScore> {
    const factors = await Promise.all([
      this.scoreCompleteness(workflow, requirements),
      this.scoreEfficiency(workflow),
      this.scoreReliability(workflow),
      this.scoreCompatibility(workflow),
      this.scoreMaintainability(workflow)
    ]);

    return {
      workflow: workflow,
      score: factors.reduce((sum, factor) => sum + factor.score, 0),
      reasoning: factors.map(f => f.reasoning).join('; ')
    };
  }
}

class AdaptiveOptimizer {
  private monitor: PerformanceMonitor;
  private optimizer: ConfigurationOptimizer;

  async startMonitoring(): Promise<void> {
    this.monitor.on('performance.degraded', async (event) => {
      const optimization = await this.optimizer.generateOptimization(event);
      await this.applyOptimization(optimization);
    });

    this.monitor.on('usage.pattern.changed', async (event) => {
      const adaptation = await this.optimizer.generateAdaptation(event);
      await this.applyAdaptation(adaptation);
    });
  }

  private async applyOptimization(optimization: Optimization): Promise<void> {
    switch (optimization.type) {
      case 'server_replacement':
        await this.replaceServer(optimization.old_server, optimization.new_server);
        break;
      case 'workflow_reorganization':
        await this.reorganizeWorkflow(optimization.new_workflow);
        break;
      case 'parameter_tuning':
        await this.tuneParameters(optimization.parameters);
        break;
    }
  }
}

class CommunityCurator {
  private feedback: FeedbackDatabase;
  private curator: IntelligentCurator;

  async collectFeedback(usage: UsageData): Promise<void> {
    // Analyze usage patterns
    const patterns = this.analyzePatterns(usage);

    // Identify successful configurations
    const successes = this.identifySuccesses(patterns);

    // Extract best practices
    const practices = this.extractBestPractices(successes);

    // Update community knowledge base
    await this.updateCommunityKnowledge(practices);
  }

  async getCommunityInsights(server: MCPServer): Promise<CommunityInsights> {
    const feedback = await this.feedback.getServerFeedback(server);
    const patterns = await this.feedback.getUsagePatterns(server);
    const alternatives = await this.feedback.getAlternatives(server);

    return {
      community_rating: feedback.averageRating,
      common_use_cases: patterns.useCases,
      known_issues: feedback.issues,
      recommended_alternatives: alternatives,
      pro_tips: feedback.tips
    };
  }
}

class LearningEngine {
  async learnFromUsage(usage: UsageData): Promise<void> {
    // Identify successful patterns
    const patterns = this.extractSuccessPatterns(usage);

    // Update recommendation algorithms
    this.updateRecommendationEngine(patterns);

    // Improve server evaluations
    this.updateServerScores(usage);

    // Enhance workflow optimizations
    this.improveWorkflowTemplates(usage);
  }
}

// Minimal Discovery System
interface MinimalDiscoverySystem {
  scanner: ServerScanner;
  evaluator: ServerEvaluator;
  recommender: SimpleRecommender;
}

async function createDiscoverySystem(): Promise<MinimalDiscoverySystem> {
  const scanner = new GitHubScanner(); // Start with GitHub
  const evaluator = new BasicEvaluator(); // Simple scoring
  const recommender = new SimpleRecommender(); // Basic recommendations

  return { scanner, evaluator, recommender };
}